Dominican Republic

The Dominican Republic economy relies on industries such as tourism, agriculture, and manufacturing, which are vulnerable to the impacts of climate change. Rising sea levels, increased storm intensity, and changing precipitation patterns pose risks to coastal infrastructure, agricultural productivity, and water resources (WHO, 2021). The Dominican Republic has demonstrated commitment to addressing climate change, with the government implementing policies and initiatives to promote renewable energy, improve environmental regulations, and enhance resilience in vulnerable areas. However, challenges related to political coordination and funding hinder comprehensive climate change strategies. Environmentally, the nation's diverse ecosystems, including coral reefs and mangroves, are threatened by climate change, leading to habitat loss, coastal erosion, and loss of biodiversity. Adaptation efforts require sustainable land and water management practices, conservation measures, and protecting natural resources (de Municipios et al, 2017).

In 2020, the country's total emissions amounted to 40 MtCO2eq, representing 0.08% of global emissions. Comparing this to the year 2000, emissions have doubled. Agriculture, Electricity, Transportation, and Industrial Processes were the main emitting sectors, contributing 26, 25, 20, and 10% respectively (Climate Watch Data, 2020a).

The Dominican Republic's updated NDC aims to achieve a reduction of 27% compared to BAU by 2030 (UNFCCC, 2022b). To meet its commitments, the Dominican Republic is implementing several actions across economic sectors, via incentivizing private investment. In the electricity and heat sector, the country has established a renewable energy target that aims to prioritize the development of grid infrastructure and electricity storage. In transport, there is support for biofuels and implementing a tax on fuel and/or emissions. However, in the agriculture and forestry, industry, and buildings sectors, no specific mitigation actions have been articulated (Climate Policy Database, 2019a). The Dominican Republic has not yet submitted an LTS to outline its comprehensive plans for achieving climate neutrality and sustainability in the long run (UNFCCC, 2023b).

Our findings for the Dominican Republic meaningfully differ from regional findings. First, Figure 5.6 shows that individual transformation categories (Incremental Improvements, Supply-Side Solutions, and Changing Consumption) can all help the country reduce emissions by at least 27% compared to Traditional Development. At the same time, it is possible to see those efforts will not be enough to achieve carbon neutrality.

None of the model development strategies achieve carbon neutrality by 2050, not even if All Actions are taken. Compared to the entire region—or Brazil described in the previous section—energy production, transport, and industry in the Dominican Republic play a much larger role in present emissions. The country also has less potential to offset emissions through afforestation. If the land-constrained island is to pursue a domestic net-zero target, more emissions reductions than what we modeled are required by 2050 in agriculture, transportation, and waste management.
Dominican Republic

The Dominican Republic economy relies on industries such as tourism, agriculture, and manufacturing, which are vulnerable to the impacts of climate change. Rising sea levels, increased storm intensity, and changing precipitation patterns pose risks to coastal infrastructure, agricultural productivity, and water resources (WHO, 2021). The Dominican Republic has demonstrated commitment to addressing climate change, with the government implementing policies and initiatives to promote renewable energy, improve environmental regulations, and enhance resilience in vulnerable areas. However, challenges related to political coordination and funding hinder comprehensive climate change strategies. Environmentally, the nation's diverse ecosystems, including coral reefs and mangroves, are threatened by climate change, leading to habitat loss, coastal erosion, and loss of biodiversity. Adaptation efforts require sustainable land and water management practices, conservation measures, and protecting natural resources (de Municipios et al, 2017).

In 2020, the country's total emissions amounted to 40 MtCO2eq, representing 0.08% of global emissions. Comparing this to the year 2000, emissions have doubled. Agriculture, Electricity, Transportation, and Industrial Processes were the main emitting sectors, contributing 26, 25, 20, and 10% respectively (Climate Watch Data, 2020a).

The Dominican Republic's updated NDC aims to achieve a reduction of 27% compared to BAU by 2030 (UNFCCC, 2022b). To meet its commitments, the Dominican Republic is implementing several actions across economic sectors, via incentivizing private investment. In the electricity and heat sector, the country has established a renewable energy target that aims to prioritize the development of grid infrastructure and electricity storage. In transport, there is support for biofuels and implementing a tax on fuel and/or emissions. However, in the agriculture and forestry, industry, and buildings sectors, no specific mitigation actions have been articulated (Climate Policy Database, 2019a). The Dominican Republic has not yet submitted an LTS to outline its comprehensive plans for achieving climate neutrality and sustainability in the long run (UNFCCC, 2023b).

Our findings for the Dominican Republic meaningfully differ from regional findings. First, Figure 5.6 shows that individual transformation categories (Incremental Improvements, Supply-Side Solutions, and Changing Consumption) can all help the country reduce emissions by at least 27% compared to Traditional Development. At the same time, it is possible to see those efforts will not be enough to achieve carbon neutrality.

None of the model development strategies achieve carbon neutrality by 2050, not even if All Actions are taken. Compared to the entire region—or Brazil described in the previous section—energy production, transport, and industry in the Dominican Republic play a much larger role in present emissions. The country also has less potential to offset emissions through afforestation. If the land-constrained island is to pursue a domestic net-zero target, more emissions reductions than what we modeled are required by 2050 in agriculture, transportation, and waste management.
<figure><img src="image_55.png" /><figcaption>**Figure 5.6** Emissions under maximum implementation of transformations by category, Dominican Republic.</figcaption></figure>

For instance, our simulations show that there is room to further reduce emissions in transport. Figure 5.7 shows fuel consumption in the sector under Traditional Development and All Actions. The reductions in All Actions are a combination of reductions in energy demand resulting from higher transportation energy efficiency, increased occupancy of private vehicles, and transport mode shifts toward public transportation and non-motorized travel, and electrification of transportation and fuel switching toward hydrogen. Further reductions in emissions could be achieved if the remaining share of conventional fuels used (i.e., gasoline and diesel) is substituted by electricity or hydrogen, or if modal shift further improves. Either option might be easier to implement on the island than in the overall region.
<figure><img src="image_55.png" /><figcaption>**Figure 5.6** Emissions under maximum implementation of transformations by category, Dominican Republic.</figcaption></figure>

For instance, our simulations show that there is room to further reduce emissions in transport. Figure 5.7 shows fuel consumption in the sector under Traditional Development and All Actions. The reductions in All Actions are a combination of reductions in energy demand resulting from higher transportation energy efficiency, increased occupancy of private vehicles, and transport mode shifts toward public transportation and non-motorized travel, and electrification of transportation and fuel switching toward hydrogen. Further reductions in emissions could be achieved if the remaining share of conventional fuels used (i.e., gasoline and diesel) is substituted by electricity or hydrogen, or if modal shift further improves. Either option might be easier to implement on the island than in the overall region.
<figure><img src="image_56.png" /><figcaption>Figure 5.7 Fuel demand in transportation under selected trajectories, Dominican Republic.</figcaption></figure>

Figure 5.8 displays estimated costs and benefits for the Dominican Republic for these categories. All but Supply-Side Solutions yield robust net benefits. For Supply-Side Solutions, the benefits of avoided pollution, fuel, and other sectors' savings are equivalent to the required sector investment costs. Implementing All Actions would require investing US$30 billion between 2025 and 2050 (about 1.6% of today's GDP each year) and would yield net benefits of US$59 billion.
<figure><img src="image_56.png" /><figcaption>Figure 5.7 Fuel demand in transportation under selected trajectories, Dominican Republic.</figcaption></figure>

Figure 5.8 displays estimated costs and benefits for the Dominican Republic for these categories. All but Supply-Side Solutions yield robust net benefits. For Supply-Side Solutions, the benefits of avoided pollution, fuel, and other sectors' savings are equivalent to the required sector investment costs. Implementing All Actions would require investing US$30 billion between 2025 and 2050 (about 1.6% of today's GDP each year) and would yield net benefits of US$59 billion.
<figure><img src="image_57.png" /><figcaption>**Figure 5.8 Discounted costs and benefits under maximum implementation of transformations by category, Dominican Republic.**</figcaption></figure>

Figure 5.9 shows emissions over time and under uncertainty of 1,000 variants of the Critical Actions strategy described in Chapter 4: shifting food-production patterns, altering deforestation, decarbonizing electricity production, and electrifying transport. No variants reach net zero, confirming that while critical actions may be important for decarbonization, a different strategy may be necessary to achieve net-zero targets. Figure 5.10 shows that efforts to decarbonize bring overwhelmingly positive economic returns, even when net zero is not achieved.

We conduct a vulnerability analysis on trajectories with emissions greater than 25 MtCO2e in 2050. (We exclude net benefits in our criteria, as so few cases have negative net benefits). Our vulnerability analysis shows that two groups of factors are at play and differ from regional findings: the increase in industrial production, buildings' energy demand, and freight demand (horizontal axis) associated with development and economic growth; and the level of action taken to counteract emissions by increasing renewable energy use and electrifying transport (vertical axis). The higher the increase in economic activity in buildings, industry, and freight, the more effort needed to fuel shift and reduce emissions in the energy supplied to those sectors.
<figure><img src="image_57.png" /><figcaption>**Figure 5.8 Discounted costs and benefits under maximum implementation of transformations by category, Dominican Republic.**</figcaption></figure>

Figure 5.9 shows emissions over time and under uncertainty of 1,000 variants of the Critical Actions strategy described in Chapter 4: shifting food-production patterns, altering deforestation, decarbonizing electricity production, and electrifying transport. No variants reach net zero, confirming that while critical actions may be important for decarbonization, a different strategy may be necessary to achieve net-zero targets. Figure 5.10 shows that efforts to decarbonize bring overwhelmingly positive economic returns, even when net zero is not achieved.

We conduct a vulnerability analysis on trajectories with emissions greater than 25 MtCO2e in 2050. (We exclude net benefits in our criteria, as so few cases have negative net benefits). Our vulnerability analysis shows that two groups of factors are at play and differ from regional findings: the increase in industrial production, buildings' energy demand, and freight demand (horizontal axis) associated with development and economic growth; and the level of action taken to counteract emissions by increasing renewable energy use and electrifying transport (vertical axis). The higher the increase in economic activity in buildings, industry, and freight, the more effort needed to fuel shift and reduce emissions in the energy supplied to those sectors.
Figure 5.11's shaded area shows the vulnerable conditions that lead strategies to fail to meet the emissions threshold:

* The increase in activity in these sectors is higher than -10% of nominal assumptions.

* The level of action is collectively less than 70% of the maximum level.

These conditions hold for 78% of all trajectories that miss emissions targets, and 90% of trajectories with these conditions miss emissions targets. In more technical terms, these conditions have a 78% density and 90% coverage.

<figure><img src="image_58.png" /><figcaption>Figure 5.9 Emissions trajectories of 1,000 Critical Action variants amid uncertainty, Dominican Republic.</figcaption></figure>
Figure 5.11's shaded area shows the vulnerable conditions that lead strategies to fail to meet the emissions threshold:

* The increase in activity in these sectors is higher than -10% of nominal assumptions.

* The level of action is collectively less than 70% of the maximum level.

These conditions hold for 78% of all trajectories that miss emissions targets, and 90% of trajectories with these conditions miss emissions targets. In more technical terms, these conditions have a 78% density and 90% coverage.

<figure><img src="image_58.png" /><figcaption>Figure 5.9 Emissions trajectories of 1,000 Critical Action variants amid uncertainty, Dominican Republic.</figcaption></figure>
<figure><img src="image_59.png" /><figcaption>**Figure 5.10 Net benefits and net 2050 emissions of Critical Action variants under uncertainty, Dominican Republic.**</figcaption></figure>

<figure><img src="image_60.png" /><figcaption>**Figure 5.11 Vulnerable conditions of Critical Actions, Dominican Republic.**</figcaption></figure>
<figure><img src="image_59.png" /><figcaption>Figure 5.10 Net benefits and net 2050 emissions of Critical Action variants under uncertainty, Dominican Republic.</figcaption></figure>

<figure><img src="image_60.png" /><figcaption>Figure 5.11 Vulnerable conditions of Critical Actions, Dominican Republic.</figcaption></figure>
Mexico

Mexico's economy is tied closely to industries such as manufacturing, oil production, and tourism. Transitioning to low-carbon technologies, promoting renewable energy, and adopting sustainable practices in these sectors can contribute to emissions reduction while fostering economic growth and job creation (Raihan and Tuspekova, 2022). Mexico, in accordance with the Paris Agreement, has implemented policies to promote renewable energy, enhance energy efficiency, and reduce deforestation. However, political delays, policy inconsistencies, and inefficient implementation remain crucial challenges (Ruiz-Rivera et al, 2017).

Environmentally, Mexico's diverse ecosystems and vulnerable communities are affected by climate change impacts such as rising temperatures, water scarcity, and increased frequency of extreme weather events. Adaptation efforts have focused on ecosystem conservation, sustainable land management, and improving resilience in vulnerable areas (Escudero and Mendoza, 2021).

In 2020, Mexico's total emissions reached 670 MtCO2eq, accounting for 1.35% of global emissions. The main drivers of emissions in its NDC BAU scenario include energy, contributing 66% of emissions, followed by agriculture at 16%, waste at 8%, industrial processes at 7%, and land-use change and forestry at almost 3%. Emissions have grown by 18% compared to the baseline year of 2000.

Mexico's NDC states a commitment to reducing GHG emissions by 35% with respect to the BAU scenario and anticipates that 30% will be achieved with national resources and the remaining 5% with international cooperation. Unfortunately, Climate Action Tracker's overall rating of Mexico's climate policies and commitments is critically insufficient. The country estimates that with current policies it will be able to meet this 35% reduction objective; however, emissions will continue to rise through 2030 (Climate Action Tracker, 2022b).

To mitigate emissions, Mexico's NDC plans to implement several transformations across sectors. In the electricity and heat sector, the country has established a support scheme for renewables, focused on grid infrastructure development, explored non-renewable low-carbon alternatives, and it has implemented a carbon pricing scheme in industry. In agriculture and forestry, Mexico provides incentives to reduce deforestation, enhance afforestation and reforestation efforts, promotes sustainable agricultural practices, and sets sustainability standards for biomass use. Meanwhile the industry sector supports energy efficiency, incentives to reduce methane emissions from fuel exploration and production, and performance and equipment standards' implementation. The transport sector focuses on low-emissions transportation, support for energy-efficient vehicles, implementing a tax on fuel and/or emissions, and modal share switch. In the buildings sector, Mexico plans to implement policies to incentivize highly efficient appliances, performance, and equipment standards, building codes, and highly efficient construction.
Mexico

Mexico's economy is tied closely to industries such as manufacturing, oil production, and tourism. Transitioning to low-carbon technologies, promoting renewable energy, and adopting sustainable practices in these sectors can contribute to emissions reduction while fostering economic growth and job creation (Raihan and Tuspekova, 2022). Mexico, in accordance with the Paris Agreement, has implemented policies to promote renewable energy, enhance energy efficiency, and reduce deforestation. However, political delays, policy inconsistencies, and inefficient implementation remain crucial challenges (Ruiz-Rivera et al, 2017).

Environmentally, Mexico's diverse ecosystems and vulnerable communities are affected by climate change impacts such as rising temperatures, water scarcity, and increased frequency of extreme weather events. Adaptation efforts have focused on ecosystem conservation, sustainable land management, and improving resilience in vulnerable areas (Escudero and Mendoza, 2021).

In 2020, Mexico's total emissions reached 670 MtCO2eq, accounting for 1.35% of global emissions. The main drivers of emissions in its NDC BAU scenario include energy, contributing 66% of emissions, followed by agriculture at 16%, waste at 8%, industrial processes at 7%, and land-use change and forestry at almost 3%. Emissions have grown by 18% compared to the baseline year of 2000.

Mexico's NDC states a commitment to reducing GHG emissions by 35% with respect to the BAU scenario and anticipates that 30% will be achieved with national resources and the remaining 5% with international cooperation. Unfortunately, Climate Action Tracker's overall rating of Mexico's climate policies and commitments is critically insufficient. The country estimates that with current policies it will be able to meet this 35% reduction objective; however, emissions will continue to rise through 2030 (Climate Action Tracker, 2022b).

To mitigate emissions, Mexico's NDC plans to implement several transformations across sectors. In the electricity and heat sector, the country has established a support scheme for renewables, focused on grid infrastructure development, explored non-renewable low-carbon alternatives, and it has implemented a carbon pricing scheme in industry. In agriculture and forestry, Mexico provides incentives to reduce deforestation, enhance afforestation and reforestation efforts, promotes sustainable agricultural practices, and sets sustainability standards for biomass use. Meanwhile the industry sector supports energy efficiency, incentives to reduce methane emissions from fuel exploration and production, and performance and equipment standards' implementation. The transport sector focuses on low-emissions transportation, support for energy-efficient vehicles, implementing a tax on fuel and/or emissions, and modal share switch. In the buildings sector, Mexico plans to implement policies to incentivize highly efficient appliances, performance, and equipment standards, building codes, and highly efficient construction.
Mexico's LTS proposes to achieve a 50% reduction in emissions from 2000 levels by 2050. The strategy aligns various sectors such as land-use planning, urban development, sustainable buildings, energy, transport, waste management, and water policies. One review of this LTS identifies weaknesses, including the lack of a net-zero target and the need for more specific mitigation and adaptation actions (Climate Action Tracker, 2022b).

Our analysis complements Mexico's LTS by providing insights on how to combine transformations across the economy to achieve carbon neutrality. Figure 5.12 shows emissions in Mexico under each category of transformations. To the extent that Traditional Development reflects Mexico's NDC BAU, all individual categories of transformations would achieve the 35% emission reduction targets expressed in the NDC. However, no individual category achieves net-zero emissions on its own by 2050. If Mexico decided to reach net-zero emissions by 2050, a more comprehensive approach closer to All Actions probably would be needed.

<figure><img src="image_61.png" /><figcaption>Figure 5.12 Emissions under maximum implementation of transformations by category, Mexico.</figcaption></figure>
Mexico's LTS proposes to achieve a 50% reduction in emissions from 2000 levels by 2050. The strategy aligns various sectors such as land-use planning, urban development, sustainable buildings, energy, transport, waste management, and water policies. One review of this LTS identifies weaknesses, including the lack of a net-zero target and the need for more specific mitigation and adaptation actions (Climate Action Tracker, 2022b).

Our analysis complements Mexico's LTS by providing insights on how to combine transformations across the economy to achieve carbon neutrality. Figure 5.12 shows emissions in Mexico under each category of transformations. To the extent that Traditional Development reflects Mexico's NDC BAU, all individual categories of transformations would achieve the 35% emission reduction targets expressed in the NDC. However, no individual category achieves net-zero emissions on its own by 2050. If Mexico decided to reach net-zero emissions by 2050, a more comprehensive approach closer to All Actions probably would be needed.

<figure><img src="image_61.png" /><figcaption>Figure 5.12 Emissions under maximum implementation of transformations by category, Mexico.</figcaption></figure>
Figure 5.13 shows that all categories of transformations result in positive net benefits under nominal assumptions about uncertainties. The most significant benefits are generated in fuel costs savings and health, safety, and productivity benefits. Enabling the transformations in All Actions requires sector-specific investments on the order of US$272 billion from 2025-2050 (approximately 1% of today's GDP per year) and yields net benefits of US$659 billion.

<figure><img src="image_62.png" /><figcaption>Figure 5.13 Discounted costs and benefits under maximum implementation of transformations by category, Mexico.</figcaption></figure>

Figures 5.14 and 5.15 present results under uncertainty for Mexico and provide country-level versions of our Chapter 4 analysis. Figure 5.14 shows the emissions over time and under uncertainty of 1,000 variants of the Critical Actions strategy described in Chapter 4. Contrary to what happens in Brazil or the region as whole, we scarcely find strategies that robustly reach net-negative emissions.

We set a threshold of 250 MtCO2e by 2050 (down from more than 1,200 MtCO2e in the traditional development trajectory) to identify strategies that reduce emissions the most. Approximately 60% of these variants reduce emissions below the threshold, while 40% of trajectories are above it. Figure 5.15 shows that implementing the four critical actions results in net benefits in the preponderance of variants and conditions we examine.
Figure 5.13 shows that all categories of transformations result in positive net benefits under nominal assumptions about uncertainties. The most significant benefits are generated in fuel costs savings and health, safety, and productivity benefits. Enabling the transformations in All Actions requires sector-specific investments on the order of US$272 billion from 2025-2050 (approximately 1% of today's GDP per year) and yields net benefits of US$659 billion.

<figure><img src="image_62.png" /><figcaption>Figure 5.13 Discounted costs and benefits under maximum implementation of transformations by category, Mexico.</figcaption></figure>

Figures 5.14 and 5.15 present results under uncertainty for Mexico and provide country-level versions of our Chapter 4 analysis. Figure 5.14 shows the emissions over time and under uncertainty of 1,000 variants of the Critical Actions strategy described in Chapter 4. Contrary to what happens in Brazil or the region as whole, we scarcely find strategies that robustly reach net-negative emissions.

We set a threshold of 250 MtCO2e by 2050 (down from more than 1,200 MtCO2e in the traditional development trajectory) to identify strategies that reduce emissions the most. Approximately 60% of these variants reduce emissions below the threshold, while 40% of trajectories are above it. Figure 5.15 shows that implementing the four critical actions results in net benefits in the preponderance of variants and conditions we examine.
<figure><img src="image_63.png" /><figcaption>Figure 5.14 Total greenhouse gas emissions' trajectories under uncertainty across strategies, Mexico.</figcaption></figure>

<figure><img src="image_64.png" /><figcaption>Figure 5.15 Net benefits and net 2050 emissions of Critical Action variants under uncertainty, Mexico.</figcaption></figure>
<figure><img src="image_63.png" /><figcaption>Figure 5.14 Total greenhouse gas emissions’ trajectories under uncertainty across strategies, Mexico.</figcaption></figure>

<figure><img src="image_64.png" /><figcaption>Figure 5.15 Net benefits and net 2050 emissions of Critical Action variants under uncertainty, Mexico.</figcaption></figure>
Figure 5.16 identifies the combinations of conditions that lead Critical Actions to miss net-zero targets in Mexico. Here, a different set of vulnerable conditions emerges than in either the region or in other countries. For Mexico, reductions in the average change in secondary forest sequestration rates (horizontal axis) is a key threat to reaching net zero, although the signal is noisy. Worsening rates require higher intensity of specific key transformations: shifts in production and consumption of food, transitions to renewable energy, and electrifying transport are key determinants of reaching low emissions.

Figure 5.16 highlights a region in the lower left corner where even higher-than-nominal sequestration rates can threaten deep reduction targets if these transformations are implemented at low intensity. Here, 95% of the trajectories in this region fail to reach the emissions threshold, and 53% of the total trajectories that miss it are in this region.

<figure><img src="image_65.png" /><figcaption>Figure 5.16 Vulnerable conditions of Critical Actions, Mexico.</figcaption></figure>
Figure 5.16 identifies the combinations of conditions that lead Critical Actions to miss net-zero targets in Mexico. Here, a different set of vulnerable conditions emerges than in either the region or in other countries. For Mexico, reductions in the average change in secondary forest sequestration rates (horizontal axis) is a key threat to reaching net zero, although the signal is noisy. Worsening rates require higher intensity of specific key transformations: shifts in production and consumption of food, transitions to renewable energy, and electrifying transport are key determinants of reaching low emissions.

Figure 5.16 highlights a region in the lower left corner where even higher-than-nominal sequestration rates can threaten deep reduction targets if these transformations are implemented at low intensity. Here, 95% of the trajectories in this region fail to reach the emissions threshold, and 53% of the total trajectories that miss it are in this region.

<figure><img src="image_65.png" /><figcaption>Figure 5.16 Vulnerable conditions of Critical Actions, Mexico.</figcaption></figure>
<figure><img src="image_66.png" /></figure>
<figure><img src="image_66.png" /></figure>
In this study, we analyzed options for Latin America and the Caribbean to robustly meet two development goals: reaching net-zero emissions in alignment with Paris Agreement targets and providing net social, economic, and environmental benefits to its people. In this chapter, we review our findings and then discuss the barriers that may need to be overcome to realize those goals, and how this study can inform country-level decarbonization plans to do so. We conclude with a review of limitations that point to next steps.

**Key Regional Findings**

We find that reaching net-zero emissions by 2050 at the regional level is technically doable and can confer enormous benefits of up to $2.7 trillion to the region, under nominal assumptions and a strategy that takes all available decarbonization actions, compared to development that follows historical patterns.

However, doing everything everywhere all at once is not feasible or necessary. Local particularities means that each country willing to take this path would need to develop its own strategy, based on its priorities and capacity. The uncertainty that surrounds any plan for 2050 means that LTSs and NDCs will need to be updated regularly, based on consultations with stakeholders and considering the latest available data and science.

Notwithstanding these caveats, our analysis of thousands of pathways suggests that any robust regional path to net zero requires significantly implementing at least four key actions:

1. ending deforestation;

2. moderating the increase of, or even decreasing, beef production;

3. fuel shifting transport toward electricity and hydrogen; and

4. producing electricity and hydrogen with renewables.

Ending deforestation is critical to ending land-use conversion, a huge source of regional emissions. Doing so can turn the region's forests from a net source of emissions to a net sink. However, the sink must be large enough to offset residual emissions from the rest of the economy. For this, afforestation is necessary and occurs by shifting food production away from land-intensive foods, particularly beef, and toward other foods.

Transport is one of the region's fastest-increasing sources of emissions, given growing demands for private vehicle travel. Our analysis points to fuel shifts in transport as a key, as it reduces emissions even if shifts in mode and activity were constrained. Fuel shifts in transport and in any other sector abate emissions only to the extent that electricity and hydrogen—the main replacement fuels—are produced with renewables and not fossil fuels. So, cleaning the grid and producing green hydrogen are the fourth critical action in a region-wide decarbonization strategy. A recent study from the IEA suggests the world is pivoting toward electric vehicles and renewables, and the key question is now whether these changes will go fast enough and far enough in the coming years (2023).
In this study, we analyzed options for Latin America and the Caribbean to robustly meet two development goals: reaching net-zero emissions in alignment with Paris Agreement targets and providing net social, economic, and environmental benefits to its people. In this chapter, we review our findings and then discuss the barriers that may need to be overcome to realize those goals, and how this study can inform country-level decarbonization plans to do so. We conclude with a review of limitations that point to next steps.

## Key Regional Findings

We find that reaching net-zero emissions by 2050 at the regional level is technically doable and can confer enormous benefits of up to $2.7 trillion to the region, under nominal assumptions and a strategy that takes all available decarbonization actions, compared to development that follows historical patterns.

However, doing everything everywhere all at once is not feasible or necessary. Local particularities means that each country willing to take this path would need to develop its own strategy, based on its priorities and capacity. The uncertainty that surrounds any plan for 2050 means that LTSs and NDCs will need to be updated regularly, based on consultations with stakeholders and considering the latest available data and science.

Notwithstanding these caveats, our analysis of thousands of pathways suggests that any robust regional path to net zero requires significantly implementing at least four key actions:

1. ending deforestation;

2. moderating the increase of, or even decreasing, beef production;

3. fuel shifting transport toward electricity and hydrogen; and

4. producing electricity and hydrogen with renewables.

Ending deforestation is critical to ending land-use conversion, a huge source of regional emissions. Doing so can turn the region's forests from a net source of emissions to a net sink. However, the sink must be large enough to offset residual emissions from the rest of the economy. For this, afforestation is necessary and occurs by shifting food production away from land-intensive foods, particularly beef, and toward other foods.

Transport is one of the region's fastest-increasing sources of emissions, given growing demands for private vehicle travel. Our analysis points to fuel shifts in transport as a key, as it reduces emissions even if shifts in mode and activity were constrained. Fuel shifts in transport and in any other sector abate emissions only to the extent that electricity and hydrogen—the main replacement fuels—are produced with renewables and not fossil fuels. So, cleaning the grid and producing green hydrogen are the fourth critical action in a region-wide decarbonization strategy. A recent study from the IEA suggests the world is pivoting toward electric vehicles and renewables, and the key question is now whether these changes will go fast enough and far enough in the coming years (2023).
To reach net zero, these four actions must be coupled with other changes across the economy, such as increasing building energy efficiency, shifting to heat pumps and public transit, using materials more efficiently, and destroying potent F-gases. Each such transformation has a modest but important impact on regional emissions and may be more or less critical to reducing emissions or unlocking socioeconomic benefits in individual countries.

A regional strategy with these features is still vulnerable to missing net zero when the area of forest needed to offset residual emissions is less than what is available. This can in a future with low livestock productivity and lower forest sequestration rates. Lower livestock productivity increases the amount of area needed for raising livestock, thereby increasing pressures to deforest. Reduced sequestration means that even more existing forests must be protected and more grazeland and feed crops need to be returned to secondary forests. Hedging against these risks requires more intense efforts to end deforestation and reduce livestock production.

Importantly, climate change exacerbates these stressors. Aside from the negative impact of human-induced deforestation, studies show that more intense dry seasons already are placing stress on Amazonian ecosystems and may limit their ability to store carbon (Gatti et al. 2021). Increasing heat stress, declining water availability, pathogens, and declining food availability are among the many potential stressors climate change places on livestock productivity (Rojas-Downing et al., 2017). However, livestock productivity is both an exogenous stressor and an endogenous action. Many techniques that farmers in the region use today are not state-of-the-art, so switching to new agricultural practices that generate higher yields can produce more food on less land. There are also high rates of food losses in the supply chain and food waste at the retail and consumer level, and reducing those losses and wastes would reduce the quantity that needs to be produced on field in the first place. Collectively, these actions reduce the need for more land and preserve the value of forest ecosystem services as a co-benefit (Searchinger et al., 2019; Dumas et al, 2022), while also better meeting the population's dietary and health needs.

Finally, robust decarbonization can confer net benefits of up to $2 trillion to the region (discounted at 7%), with a median net benefit of $1 trillion across all the deeply uncertain conditions we explored. These benefits are net of substantial technical investments, which range from $0.5 to $1.5 trillion depending upon the future. The largest consistent benefits are in the form of fuel-cost savings; health, productivity, and safety benefits; and ecosystem services.

## Barriers to Change

Our analysis suggests that Latin America and the Caribbean can transition to a net-zero economy while enjoying better development, with tremendous gains to social, economic, and environmental outcomes. Given this, it is logical to ask why these transformations are not occurring at a pace that these results might warrant.
To reach net zero, these four actions must be coupled with other changes across the economy, such as increasing building energy efficiency, shifting to heat pumps and public transit, using materials more efficiently, and destroying potent F-gases. Each such transformation has a modest but important impact on regional emissions and may be more or less critical to reducing emissions or unlocking socioeconomic benefits in individual countries.

A regional strategy with these features is still vulnerable to missing net zero when the area of forest needed to offset residual emissions is less than what is available. This can in a future with low livestock productivity and lower forest sequestration rates. Lower livestock productivity increases the amount of area needed for raising livestock, thereby increasing pressures to deforest. Reduced sequestration means that even more existing forests must be protected and more grazeland and feed crops need to be returned to secondary forests. Hedging against these risks requires more intense efforts to end deforestation and reduce livestock production.

Importantly, climate change exacerbates these stressors. Aside from the negative impact of human-induced deforestation, studies show that more intense dry seasons already are placing stress on Amazonian ecosystems and may limit their ability to store carbon (Gatti et al. 2021). Increasing heat stress, declining water availability, pathogens, and declining food availability are among the many potential stressors climate change places on livestock productivity (Rojas-Downing et al., 2017). However, livestock productivity is both an exogenous stressor and an endogenous action. Many techniques that farmers in the region use today are not state-of-the-art, so switching to new agricultural practices that generate higher yields can produce more food on less land. There are also high rates of food losses in the supply chain and food waste at the retail and consumer level, and reducing those losses and wastes would reduce the quantity that needs to be produced on field in the first place. Collectively, these actions reduce the need for more land and preserve the value of forest ecosystem services as a co-benefit (Searchinger et al., 2019; Dumas et al, 2022), while also better meeting the population's dietary and health needs.

Finally, robust decarbonization can confer net benefits of up to $2 trillion to the region (discounted at 7%), with a median net benefit of $1 trillion across all the deeply uncertain conditions we explored. These benefits are net of substantial technical investments, which range from $0.5 to $1.5 trillion depending upon the future. The largest consistent benefits are in the form of fuel-cost savings; health, productivity, and safety benefits; and ecosystem services.

## Barriers to Change

Our analysis suggests that Latin America and the Caribbean can transition to a net-zero economy while enjoying better development, with tremendous gains to social, economic, and environmental outcomes. Given this, it is logical to ask why these transformations are not occurring at a pace that these results might warrant.
A host of regulatory, fiscal, information, and other barriers stand in the way of changes that would lead to better development (Fazekas et al., 2022). For example, the widespread subsidies associated with fossil fuels (International Monetary Fund, 2021) entrench the use of fossil fuels in transport and energy production, even when renewables are the more cost-effective alternative. Poor government oversight and enforcement of existing laws and regulations can weaken protections already in place for forests and create land grabs that accelerate deforestation, yielding near-term gains at the expense of long-term sustainability (Carrero et al., 2022). Even the built environment can pose barriers to change: urban sprawl enabled by historical emphasis on road travel and an absence of bike lanes and walking paths can make it difficult to shift development toward walking, biking, transit, and other sustainable modes (Mouratidis et al., 2019).

Compounding the challenge is that the costs and benefits of transformations are borne differently by various people or actors, and many of the largest costs are internalized while many of the benefits remain external to market forces. As one example, a shift in food production and consumption from livestock to crops, with attendant dietary improvements, could offer massive and widespread benefits in terms of productivity, nutritional benefits and health cost savings, and potentially grocery savings (Springmann et al., 2016). However, it would present costs to the livestock sector in terms of foregone output, which can generate resistance from powerful industries (Merrigan et al., 2015) and challenge poverty reduction efforts in rural areas. Meanwhile the benefits of healthier diets accrue to households and to the healthcare system over the long term. As another example, the costs of expanding transit generally fall to public transit authorities who face a host of budgetary and regulatory constraints. The benefits of transit, in contrast, are reaped not only by transit users, but the general population that may benefit from greater mobility, lower congestion, safer roads, and cleaner air. In sum, costs are often distinct, measurable, and acutely felt by certain groups, while the benefits are diffuse, hard to measure, and broadly experienced by a much larger group.

Timing is another issue. While a transition to net-zero progressively departs, in terms of emissions, from a traditional development trend, changing course often requires large initial investments driven by a long-term vision (Vogt-Schilb et al., 2018). For some transformations, countries may rely on international capital. Many governments in the region, for instance, have designed power generation and public transport markets that let foreign compagnies invest in solar panels, windmills, or electric buses at no or little upfront cost for the countries, being funded instead over time by rate payers. Our research does not investigate such programs’ large-scale feasibility, nor their macroeconomic impact.

## Developing Long-Term Decarbonization Plans for Individual Countries

Our research shows that Latin America and the Caribbean can reach net-zero emissions in 2050 at a net benefit, and that four key actions form decarbonization strategies' backbone. However, our work also shows that one size does not fit all. Countries differ in many ways, such as whether they produce substantial amounts of fossil fuels and cattle, whether they have room for massive reforestation, and how their built environment shapes efforts to decarbonize transport. This shapes the extent to which regionally important actions make sense nationally.
A host of regulatory, fiscal, information, and other barriers stand in the way of changes that would lead to better development (Fazekas et al., 2022). For example, the widespread subsidies associated with fossil fuels (International Monetary Fund, 2021) entrench the use of fossil fuels in transport and energy production, even when renewables are the more cost-effective alternative. Poor government oversight and enforcement of existing laws and regulations can weaken protections already in place for forests and create land grabs that accelerate deforestation, yielding near-term gains at the expense of long-term sustainability (Carrero et al., 2022). Even the built environment can pose barriers to change: urban sprawl enabled by historical emphasis on road travel and an absence of bike lanes and walking paths can make it difficult to shift development toward walking, biking, transit, and other sustainable modes (Mouratidis et al., 2019).

Compounding the challenge is that the costs and benefits of transformations are borne differently by various people or actors, and many of the largest costs are internalized while many of the benefits remain external to market forces. As one example, a shift in food production and consumption from livestock to crops, with attendant dietary improvements, could offer massive and widespread benefits in terms of productivity, nutritional benefits and health cost savings, and potentially grocery savings (Springmann et al., 2016). However, it would present costs to the livestock sector in terms of foregone output, which can generate resistance from powerful industries (Merrigan et al., 2015) and challenge poverty reduction efforts in rural areas. Meanwhile the benefits of healthier diets accrue to households and to the healthcare system over the long term. As another example, the costs of expanding transit generally fall to public transit authorities who face a host of budgetary and regulatory constraints. The benefits of transit, in contrast, are reaped not only by transit users, but the general population that may benefit from greater mobility, lower congestion, safer roads, and cleaner air. In sum, costs are often distinct, measurable, and acutely felt by certain groups, while the benefits are diffuse, hard to measure, and broadly experienced by a much larger group.

Timing is another issue. While a transition to net-zero progressively departs, in terms of emissions, from a traditional development trend, changing course often requires large initial investments driven by a long-term vision (Vogt-Schilb et al., 2018). For some transformations, countries may rely on international capital. Many governments in the region, for instance, have designed power generation and public transport markets that let foreign compagnies invest in solar panels, windmills, or electric buses at no or little upfront cost for the countries, being funded instead over time by rate payers. Our research does not investigate such programs’ large-scale feasibility, nor their macroeconomic impact.

## Developing Long-Term Decarbonization Plans for Individual Countries

Our research shows that Latin America and the Caribbean can reach net-zero emissions in 2050 at a net benefit, and that four key actions form decarbonization strategies' backbone. However, our work also shows that one size does not fit all. Countries differ in many ways, such as whether they produce substantial amounts of fossil fuels and cattle, whether they have room for massive reforestation, and how their built environment shapes efforts to decarbonize transport. This shapes the extent to which regionally important actions make sense nationally.
Each country will have to build its own vision of a low-emission future, make its own plans on how to get there, and diagnose the barriers preventing their countries from capitalizing on a future with a healthier population, healthier environment, and stronger economy. Like a picture coming into focus, the cost and benefit implications of decarbonization will also become clearer at the national level. For instance, while the regional strategy to decarbonize transport leans heavily on fuel shifting, a country with high urban density and a walkable environment might find mode shifting transport and reducing transport demand to be a cheaper and more beneficial alternative.

Countries face a common set of challenges in decarbonization planning (Calfucoy et al., 2022). Their successful long-term decarbonization planning shares some characteristics (Climate Action Tracker, 2019; IDB, 2019; World Bank, 2023; Jaramillo et al., 2023):

* Plans should integrate and jointly address decarbonization and development goals.

* All stakeholders from across the government and private sector should be consulted in framing those goals and in articulating actions, constraints, and uncertainties and providing inputs in the form of data, models, and analytical tools.

* Actions should be described in terms that are meaningful and actionable for those who must implement them.

* Actions should be evaluated in terms of not only their emissions effects, but also their macroeconomic effects, including costs and benefits to sector actors and to society overall.

* The assessments should include all sectors and all greenhouse gases and make transparent and scientifically grounded assessments of how they will be reduced.

* Planning should make uncertainty explicit, identify how plans may be vulnerable to assumptions or changing conditions, and identify actions to make them more robust.

Our approach in this study facilitates decarbonization planning and processes with these characteristics. As Chapter 2 describes, SiSePuede is a toolbox for developing and evaluating decarbonization strategies, and it is embedded in a Robust Decision Making methodology, which provides an interactive stakeholder engagement process, an approach for developing large ensembles of futures to account for uncertainty, and techniques for analyzing hundreds of scenarios to identify and mitigate key vulnerabilities of different strategies.

SiSePuede can be run for any country or region in the world. It integrates and is calibrated against publicly available global datasets from the IEA, IMF, FAO, World Bank, OECD, IDB, and others. Where data are missing or erroneous, SiSePuede imputes data points based on information from countries similar in size, geography, level of development, climate, and other relevant factors. SiSePuede is also open source, scalable, non-proprietary, and publicly available.

Initial country-level analyses such as those shown in this study can be generated quickly and used as a concrete starting place for LTS discussions with the full suite of stakeholders from sector and transverse government actors and the private sector. They can recommend transformations, uncertainties, and metrics designed for country conditions, goals, and constraints. Then analyses can be tailored and iterated upon with country-specific data and models held by national agencies, academics, and others.
Each country will have to build its own vision of a low-emission future, make its own plans on how to get there, and diagnose the barriers preventing their countries from capitalizing on a future with a healthier population, healthier environment, and stronger economy. Like a picture coming into focus, the cost and benefit implications of decarbonization will also become clearer at the national level. For instance, while the regional strategy to decarbonize transport leans heavily on fuel shifting, a country with high urban density and a walkable environment might find mode shifting transport and reducing transport demand to be a cheaper and more beneficial alternative.

Countries face a common set of challenges in decarbonization planning (Calfucoy et al., 2022). Their successful long-term decarbonization planning shares some characteristics (Climate Action Tracker, 2019; IDB, 2019; World Bank, 2023; Jaramillo et al., 2023):

* Plans should integrate and jointly address decarbonization and development goals.

* All stakeholders from across the government and private sector should be consulted in framing those goals and in articulating actions, constraints, and uncertainties and providing inputs in the form of data, models, and analytical tools.

* Actions should be described in terms that are meaningful and actionable for those who must implement them.

* Actions should be evaluated in terms of not only their emissions effects, but also their macroeconomic effects, including costs and benefits to sector actors and to society overall.

* The assessments should include all sectors and all greenhouse gases and make transparent and scientifically grounded assessments of how they will be reduced.

* Planning should make uncertainty explicit, identify how plans may be vulnerable to assumptions or changing conditions, and identify actions to make them more robust.

Our approach in this study facilitates decarbonization planning and processes with these characteristics. As Chapter 2 describes, SiSePuede is a toolbox for developing and evaluating decarbonization strategies, and it is embedded in a Robust Decision Making methodology, which provides an interactive stakeholder engagement process, an approach for developing large ensembles of futures to account for uncertainty, and techniques for analyzing hundreds of scenarios to identify and mitigate key vulnerabilities of different strategies.

SiSePuede can be run for any country or region in the world. It integrates and is calibrated against publicly available global datasets from the IEA, IMF, FAO, World Bank, OECD, IDB, and others. Where data are missing or erroneous, SiSePuede imputes data points based on information from countries similar in size, geography, level of development, climate, and other relevant factors. SiSePuede is also open source, scalable, non-proprietary, and publicly available.

Initial country-level analyses such as those shown in this study can be generated quickly and used as a concrete starting place for LTS discussions with the full suite of stakeholders from sector and transverse government actors and the private sector. They can recommend transformations, uncertainties, and metrics designed for country conditions, goals, and constraints. Then analyses can be tailored and iterated upon with country-specific data and models held by national agencies, academics, and others.
Active participation during the LTS formulation stage from those who will need to implement change is essential to inform analysis and increase buy-in: policymakers often do not buy into plans that they had no role in developing (Niet et al., 2021). By engaging early in the process, their insights, expertise, and concerns can shape the plan and ease its delivery. SiSePuede and RDM together provide a flexible process and tools to ensure that stakeholders’ priorities and viewpoints are integrated into the analysis, increasing national ownership of resulting plans.

## Next Steps

This study has several limitations that also point the way to the next steps. Our analysis focuses primarily on mainstream decarbonization actions. However, various promising innovations merit inclusion in future analyses, such as producing zero emissions steel with hydrogen-based direct reduction iron (Wang et al., 2021) or using wind propulsion for large-scale maritime operations (Chou et al., 2021).

There are also important questions to be answered regarding equity, jobs, and environmental justice. While much research shows that actions to reduce emissions in Latin America and the Caribbean also could create jobs, the flow of job creation and loss in different sectors is an important metric for assessing distributional impacts of a decarbonization strategy, and for facilitating plan support and success (Saget et al., 2020; Feng et al., 2023; Alfonso et al., 2023).

While our study answers many questions about decarbonization strategies for Latin America and the Caribbean, it also raises new questions. Smart climate financing will be important for enabling the many technical changes required, even when those changes confer savings in the longer term. A future use of SiSePuede is to examine the effects of decarbonization on tax expenditures and revenues: How quickly will energy savings reduce fossil fuel subsidies? What could replace gasoline and diesel taxes once car fleets go electric? Which economic activities stand to decline and what could replace them? How will lower health costs reflect in increased productivity, economic activity, and profit taxes? SiSePuede can help shed light on these questions, which will no doubt be important to finance ministries as their countries embark on paths to decarbonize.
Active participation during the LTS formulation stage from those who will need to implement change is essential to inform analysis and increase buy-in: policymakers often do not buy into plans that they had no role in developing (Niet et al., 2021). By engaging early in the process, their insights, expertise, and concerns can shape the plan and ease its delivery. SiSePuede and RDM together provide a flexible process and tools to ensure that stakeholders’ priorities and viewpoints are integrated into the analysis, increasing national ownership of resulting plans.

## Next Steps

This study has several limitations that also point the way to the next steps. Our analysis focuses primarily on mainstream decarbonization actions. However, various promising innovations merit inclusion in future analyses, such as producing zero emissions steel with hydrogen-based direct reduction iron (Wang et al., 2021) or using wind propulsion for large-scale maritime operations (Chou et al., 2021).

There are also important questions to be answered regarding equity, jobs, and environmental justice. While much research shows that actions to reduce emissions in Latin America and the Caribbean also could create jobs, the flow of job creation and loss in different sectors is an important metric for assessing distributional impacts of a decarbonization strategy, and for facilitating plan support and success (Saget et al., 2020; Feng et al., 2023; Alfonso et al., 2023).

While our study answers many questions about decarbonization strategies for Latin America and the Caribbean, it also raises new questions. Smart climate financing will be important for enabling the many technical changes required, even when those changes confer savings in the longer term. A future use of SiSePuede is to examine the effects of decarbonization on tax expenditures and revenues: How quickly will energy savings reduce fossil fuel subsidies? What could replace gasoline and diesel taxes once car fleets go electric? Which economic activities stand to decline and what could replace them? How will lower health costs reflect in increased productivity, economic activity, and profit taxes? SiSePuede can help shed light on these questions, which will no doubt be important to finance ministries as their countries embark on paths to decarbonize.
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Audoly, R., Vogt-Schilb, A., Guivarch, C., and Pfeiffer, A. 2018. Pathways toward zero-carbon electricity required for climate stabilization. Applied Energy 225: 884-901. https://doi.org/10.1016/j.apenergy.2018.05.026

Bagolle, A., Costella, C., and Goyeneche, L. 2023. Social Protection and Climate Change: How Can We Protect the most Vulnerable Households Against New Climate Threats? Inter-American Development Bank.

Bastianoni, S., Pulselli, F.M., and Tiezzi, E. 2004. "The problem of assigning responsibility for greenhouse gas emissions." Ecological Economics 49(3): 253-257.

Bataille, C., Waisman, H., Vogt-Schilb, A., Jaramillo, M., Delgado, R., Arguello, R., Clarke, L., Wild, T., Lallana, F., Bravo, G., LeTreut, G., Nadal, G., Godinez, G., Quiros-Tortos, J., Pereira, E., Howells, M., Buira, D., Tovilla, J., Farbes, J., et al. 2020. Net-zero deep decarbonization pathways in Latin America: Challenges and opportunities. Energy Strategy Reviews 30: 100510.

Beauchemin, K.A., Ungerfeld, E.M., Eckard, R.J., and Wang, M. 2020. "Fifty years of research on rumen methanogenesis: Lessons learned and future challenges for mitigation." Animal 14(S1): s2-s16.

Benavides, C., Cifuentes, L., Díaz, M., Gilabert, H., Gonzales, L., González, D., Groves, D. et al. 2021. "Options to achieve carbon neutrality in Chile: An assessment under uncertainty."
Inter-American Development Bank.

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Inter-American Development Bank.

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Binsted, M., Iyer, G., Edmonds, J., Vogt-Schilb, A., Arguello, R., Cadena, A., Delgado, R., Feijoo, F., Lucena, A.F., McJeon, H. and Miralles-Wilhelm, F. 2020. Stranded asset implications of the Paris Agreement in Latin America and the Caribbean. Environmental Research Letters 15(4): 044026.

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Climate Watch Data. 2023. Net Zero Tracker (accessed October 17, 2023). https://www.climatewatchdata.org/net-zero-tracker?locations=LAC

Commoner, B. 2013. "The environmental cost of economic growth." Energy, Economic Growth and the Environment. RFF Press, pp. 30-65.

Costanza, R., De Groot, R., Sutton, P., Van der Ploeg, S., Anderson, S.J., Kubiszewski, I., Farber, S., and Turner, R.K. 2014. "Changes in the global value of ecosystem services." Global Environmental Change 26: 152-158.

Dargay, J., Gately, D., and Sommer, M. 2007. "Vehicle ownership and income growth, worldwide: 1960-2030." The Energy Journal 28(4).

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The Benefits and Costs of
Reaching Net Zero Emissions in
Latin America and the Caribbean

Technical
Appendices
The Benefits and Costs of
Reaching Net Zero Emissions in
Latin America and the Caribbean

Technical
Appendices
SiSePuede is an open source, robust modeling framework for emission accounting based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Eggleston et al., 2006) and subsequent 2019 Revision (Buendia et al., 2019). The framework, which is written in Python and Julia, includes several analytical components used to facilitate exploratory modeling of sectoral transformations and their effects on demands and emissions, including an integrated multisector emissions model; an uncertainty quantification system based on Latin Hypercube sampling; and scalable database generation and scenario management.

The SiSePuede integrated emission model includes four emission accounting sectors—Agriculture, Forestry, and Land Use (AFOLU), Circular Economy (waste management), Energy, and Industrial Processes and Product Use (IPPU)—in addition to a shared driver sector, the Socioeconomic sector. Each sector is divided into multiple accounting subsectors, which include refined emissions that correspond to different IPCC emission accounting codes.
SiSePuede is an open source, robust modeling framework for emission accounting based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Eggleston et al., 2006) and subsequent 2019 Revision (Buendia et al., 2019). The framework, which is written in Python and Julia, includes several analytical components used to facilitate exploratory modeling of sectoral transformations and their effects on demands and emissions, including an integrated multisector emissions model; an uncertainty quantification system based on Latin Hypercube sampling; and scalable database generation and scenario management.

The SiSePuede integrated emission model includes four emission accounting sectors—Agriculture, Forestry, and Land Use (AFOLU), Circular Economy (waste management), Energy, and Industrial Processes and Product Use (IPPU)—in addition to a shared driver sector, the Socioeconomic sector. Each sector is divided into multiple accounting subsectors, which include refined emissions that correspond to different IPCC emission accounting codes.
<figure><img src="image_67.png" /></figure>

Note: Arrows indicate data flows between submodules in SiSePuede.

<caption>**Figure TA1. Technical modeling framework for SiSePuede.**</caption>
<figure><img src="image_67.png" /></figure>

Note: Arrows indicate data flows between submodules in SiSePuede.

<caption>**Figure TA1. Technical modeling framework for SiSePuede.**</caption>
AFOLU models six subsectors: agriculture, forestry, land use, livestock, livestock manure management, and soil. These six sectors are based on volume 4 of the IPCC guidance for national greenhouse gas (GHG) inventories and include extensive treatment of key emission phenomena, including crop residues and burning, forest sequestration, land-use conversion and use, enteric fermentation, manure management, fertilizer application, and soil carbon sequestration in mineral and organic soils. The land-use subsector models land-use transitions directly as a discrete Markov chain, allowing for a detailed accounting of emissions stemming from land-use conversion. Furthermore, it includes a novel mechanism, known as the land-use reallocation factor, to model land-use changes that occur in response to changing demands for livestock and crops and reconcile these demands with exogenous expectations about land-use changes. Demands for crops and livestock production generally are based on historical production, imports, and exports and are responsive to changes in trade and gross domestic product (GDP), GDP/capita, and population.

Circular Economy includes three subsectors: liquid waste, solid waste, and wastewater treatment. These three sectors are derived from volume 5 of the IPCC guidance for national greenhouse gas inventories and include detailed emissions estimates from wastewater treatment and management pathways, solid waste treatment pathways (including a first-order decay model of landfilled waste), and recycling, which then gets passed to the industrial production model to estimate changes for producing virgin materials. Waste generation primarily is driven by per-person generation factors, which are responsive to changes in GDP, GDP/capita, and population and other sectors, including livestock manure management and supply-chain loss in agriculture.

Energy includes nine model subsectors, six of which are emission subsectors: carbon capture and sequestration, energy fuels, energy storage, energy technology (fuel production, including electricity), fugitive emissions, industrial energy, stationary combustion and other energy, transportation, and transportation demands. These sectors include estimates of energy demands, emissions from fuel combustion, and fuel consumption using information from other sectors as input, including GDP, industrial production, number of households, and more. All energy demands for fuel production—including electricity, petroleum refinement, coal mining, natural gas production and processing, and hydrogen production—are passed to a least-cost optimization model developed in Julia NEMO (SEI, 2023) to estimate emissions from energy and fuel production.

IPPU is based on volume 3 of the IPCC Guidance for National Greenhouse Gas (GHG) Inventories. The IPPU sector includes estimates of emissions from a range of gases released during industrial production, including a by-gas accounting of several fluorinated compounds (including HFCs, PFCs, and other FCs) derived from other bottom-up estimates in the literature. Industrial production primarily is driven by domestic demands and trade and is responsive to changes in GDP, GDP/capita, and recycling (for applicable industries). In the SiSePuede Directed Acyclic Graph (DAG), industrial production functions are stored in IPPU and accessed in both Circular Economy and AFOLU.
AFOLU models six subsectors: agriculture, forestry, land use, livestock, livestock manure management, and soil. These six sectors are based on volume 4 of the IPCC guidance for national greenhouse gas (GHG) inventories and include extensive treatment of key emission phenomena, including crop residues and burning, forest sequestration, land-use conversion and use, enteric fermentation, manure management, fertilizer application, and soil carbon sequestration in mineral and organic soils. The land-use subsector models land-use transitions directly as a discrete Markov chain, allowing for a detailed accounting of emissions stemming from land-use conversion. Furthermore, it includes a novel mechanism, known as the land-use reallocation factor, to model land-use changes that occur in response to changing demands for livestock and crops and reconcile these demands with exogenous expectations about land-use changes. Demands for crops and livestock production generally are based on historical production, imports, and exports and are responsive to changes in trade and gross domestic product (GDP), GDP/capita, and population.

Circular Economy includes three subsectors: liquid waste, solid waste, and wastewater treatment. These three sectors are derived from volume 5 of the IPCC guidance for national greenhouse gas inventories and include detailed emissions estimates from wastewater treatment and management pathways, solid waste treatment pathways (including a first-order decay model of landfilled waste), and recycling, which then gets passed to the industrial production model to estimate changes for producing virgin materials. Waste generation primarily is driven by per-person generation factors, which are responsive to changes in GDP, GDP/capita, and population and other sectors, including livestock manure management and supply-chain loss in agriculture.

Energy includes nine model subsectors, six of which are emission subsectors: carbon capture and sequestration, energy fuels, energy storage, energy technology (fuel production, including electricity), fugitive emissions, industrial energy, stationary combustion and other energy, transportation, and transportation demands. These sectors include estimates of energy demands, emissions from fuel combustion, and fuel consumption using information from other sectors as input, including GDP, industrial production, number of households, and more. All energy demands for fuel production—including electricity, petroleum refinement, coal mining, natural gas production and processing, and hydrogen production—are passed to a least-cost optimization model developed in Julia NEMO (SEI, 2023) to estimate emissions from energy and fuel production.

IPPU is based on volume 3 of the IPCC Guidance for National Greenhouse Gas (GHG) Inventories. The IPPU sector includes estimates of emissions from a range of gases released during industrial production, including a by-gas accounting of several fluorinated compounds (including HFCs, PFCs, and other FCs) derived from other bottom-up estimates in the literature. Industrial production primarily is driven by domestic demands and trade and is responsive to changes in GDP, GDP/capita, and recycling (for applicable industries). In the SiSePuede Directed Acyclic Graph (DAG), industrial production functions are stored in IPPU and accessed in both Circular Economy and AFOLU.
Finally, the Socioeconomic sector includes two subsectors: general and economy. These two subsectors are used to manage exogenously defined drivers that are shared across emission models, such as GDP and population. The Socioeconomic subsector includes some simple calculations—such as GDP/capita, the number of households, and various rates of growth in drivers—but does not account for any emissions.

SiSePuede accounts for gas-specific emissions across several greenhouse gases, including methane, carbon dioxide, nitrous oxide (CH4, CO2, N2O), and numerous PFCs, HFCs, and other fluorinated compounds. Accounting can be set to reflect different global warming potentials (GWP), including 20-, 100-, and 500-year GWPs.

## SiSePuede Calibration

Figure 2 describes the calibration process for SiSePuede and how this interrelates with simulation runs. In the first step, two sets of input parameters are defined: a) parameters that have observed historical data and b) parameters that need to be estimated through calibration. For the latter, historical emissions are the response of interest used in the calibration process. The calibration process searches for a set of calibrated parameters that minimize the error between the model-simulated emissions and observed emissions. This process occurs separately for each sector.

A minimization problem is defined where the objective function to be minimized is the mean square error between the simulated and observed series of carbon dioxide equivalent (CO2e) emissions of each sector.

$$ \begin{array}{l} \min \frac{1}{T} \sum_{t}^{T} (CO_2 \text{ emissions}_{s,t} - CO_2 \text{ emissions}_{s,t})^2 \\ \text{s.t} \\ x_1, x_2, \dots, x_n \in (0, c), \text{ where } c \in \mathbb{R} \end{array} $$

where

* $CO_2 \text{ emissions}_{s,t}$ is the estimated CO2e SiSePuede emissions in sector s at time t;

* $CO_2 \text{ emissions}_{s,t}$ is the historical series (we used climate watch data for this study, but other sources can also be used); and

vector $x_1, x_2, \dots, x_n$ is a set of scaling factors that multiply a set of input variables to be calibrated. These calibrated parameters include factors for which we do not have data or for which we require a more reliable baseline.

Once a calibrated set of input parameters is estimated for historical conditions, this set is then projected in the future varying as two groups of parameters: a) parameters that describe how transformations will evolve over time and b) parameters that describe how exogenous trends will evolve in the future. Then this database is used as an input database for SiSePuede. For each input database, a SiSePuede simulation run will result in projected emissions, benefits, and costs over time.
Finally, the Socioeconomic sector includes two subsectors: general and economy. These two subsectors are used to manage exogenously defined drivers that are shared across emission models, such as GDP and population. The Socioeconomic subsector includes some simple calculations—such as GDP/capita, the number of households, and various rates of growth in drivers—but does not account for any emissions.

SiSePuede accounts for gas-specific emissions across several greenhouse gases, including methane, carbon dioxide, nitrous oxide (CH4, CO2, N2O), and numerous PFCs, HFCs, and other fluorinated compounds. Accounting can be set to reflect different global warming potentials (GWP), including 20-, 100-, and 500-year GWPs.

## SiSePuede Calibration

Figure 2 describes the calibration process for SiSePuede and how this interrelates with simulation runs. In the first step, two sets of input parameters are defined: a) parameters that have observed historical data and b) parameters that need to be estimated through calibration. For the latter, historical emissions are the response of interest used in the calibration process. The calibration process searches for a set of calibrated parameters that minimize the error between the model-simulated emissions and observed emissions. This process occurs separately for each sector.

A minimization problem is defined where the objective function to be minimized is the mean square error between the simulated and observed series of carbon dioxide equivalent (CO2e) emissions of each sector.

$$ \begin{array}{l} \min \frac{1}{T} \sum_{t}^{T} (CO_2 \text{ emissions}_{s,t} - CO_2 \text{ emissions}_{s,t})^2 \\ \text{s.t} \\ x_1, x_2, \dots, x_n \in (0, c), \text{ where } c \in \mathbb{R} \end{array} $$

where

* $CO_2 \text{ emissions}_{s,t}$ is the estimated CO2e SiSePuede emissions in sector s at time t;

* $CO_2 \text{ emissions}_{s,t}$ is the historical series (we used climate watch data for this study, but other sources can also be used); and

vector $x_1, x_2, \dots, x_n$ is a set of scaling factors that multiply a set of input variables to be calibrated. These calibrated parameters include factors for which we do not have data or for which we require a more reliable baseline.

Once a calibrated set of input parameters is estimated for historical conditions, this set is then projected in the future varying as two groups of parameters: a) parameters that describe how transformations will evolve over time and b) parameters that describe how exogenous trends will evolve in the future. Then this database is used as an input database for SiSePuede. For each input database, a SiSePuede simulation run will result in projected emissions, benefits, and costs over time.
<figure><img src="image_68.png" /><figcaption>Figure TA2. Calibration workflow.</figcaption></figure>

The minimization problem is solved using two bio-inspired algorithms:

• Genetic binary (Sadri et al., 2006); and

• Particle Swarm Optimization (PSO) (Wang et al., 2018).

Genetic algorithms balance exploitation and exploration. This balance is achieved by the individuals of the population's selection mechanism based on their aptitude and the genetic crossover operator. The PSO algorithm engages a group of individuals (particles) from different points in the search space, each guided by the collective action's natural life principles to find an optimal solution.

SiSePuede calibration occurs via cross-validation such that the process is repeated randomly selecting subsets of the variation in the response of interest (i.e., sectorial emissions). Figures 3 and 4 exemplify the calibration performance for all countries considered in this study across two sectors: AFOLU and Liquid Waste. The dark blue line indicates known historical data of the response of interest (i.e., sector emissions), while the light-blue lines display simulated results for different instances of cross validation. As these figures show, a successful calibration process is on which different calibration instances revolve around the historical time series of the response of interest.
<figure><img src="image_68.png" /><figcaption>Figure TA2. Calibration workflow.</figcaption></figure>

The minimization problem is solved using two bio-inspired algorithms:

• Genetic binary (Sadri et al., 2006); and

• Particle Swarm Optimization (PSO) (Wang et al., 2018).

Genetic algorithms balance exploitation and exploration. This balance is achieved by the individuals of the population's selection mechanism based on their aptitude and the genetic crossover operator. The PSO algorithm engages a group of individuals (particles) from different points in the search space, each guided by the collective action's natural life principles to find an optimal solution.

SiSePuede calibration occurs via cross-validation such that the process is repeated randomly selecting subsets of the variation in the response of interest (i.e., sectorial emissions). Figures 3 and 4 exemplify the calibration performance for all countries considered in this study across two sectors: AFOLU and Liquid Waste. The dark blue line indicates known historical data of the response of interest (i.e., sector emissions), while the light-blue lines display simulated results for different instances of cross validation. As these figures show, a successful calibration process is on which different calibration instances revolve around the historical time series of the response of interest.
<figure><img src="image_69.png" /><figcaption>Figure TA3. Agriculture, Forestry, and Land Use (AFOLU) calibration.</figcaption></figure>

<figure><img src="image_70.png" /><figcaption>Figure TA4. Liquid waste calibration.</figcaption></figure>
<figure><img src="image_69.png" /><figcaption>Figure TA3. Agriculture, Forestry, and Land Use (AFOLU) calibration.</figcaption></figure>

<figure><img src="image_70.png" /><figcaption>Figure TA4. Liquid waste calibration.</figcaption></figure>
Each instance of cross-validation is associated with a specific database of input parameters, such that, after cross-validation, for each calibration parameter, point-estimates that aggregate the behavior across the cross-validation set can be estimated. Figure 5 shows this exercise for a subset of calibration parameters. Note that since this process is carried out for each country used in the simulation, it is possible to estimate and compare mean and variance variation across countries.

Once simulations are executed, it is possible to run the model using the mean values of cross-validation as input parameters to the model. However, because using the mean value of cross-validation will result in an initial condition different to the one observed—for example, in the last available data point of historical data—it is possible to rescale the simulation data such that the simulation’s intertemporal variation reflects the initial conditions of a particular emissions trajectory more accurately.

<figure><img src="image_71.png" /><figcaption>Figure 5. Point estimate and standard deviation of estimated parameters.</figcaption></figure>

## Contrast with Other Modeling Approaches

SiSePuede takes a unique, sectoral transformation-centered approach to modeling emissions, contrasting with other common modeling techniques. For example, integrated assessment models (IAMs) are used to model biophysical emissions processes as driven by anthropogenic activity, and some include biophysical feedback. In accordance with IPCC inventory guidelines, SiSePuede bases emissions on emission factors that respond to drivers, though certain phenomena—such as methane emissions from anaerobic decomposition in solid waste landfills, which are modeled using a first-order decay model—include biophysical treatments. Biophysical feedback such as impacts of climate change on hydropower production or crop yield factors are treated as uncertain factors that can be explored within reasonable expected ranges.
Each instance of cross-validation is associated with a specific database of input parameters, such that, after cross-validation, for each calibration parameter, point-estimates that aggregate the behavior across the cross-validation set can be estimated. Figure 5 shows this exercise for a subset of calibration parameters. Note that since this process is carried out for each country used in the simulation, it is possible to estimate and compare mean and variance variation across countries.

Once simulations are executed, it is possible to run the model using the mean values of cross-validation as input parameters to the model. However, because using the mean value of cross-validation will result in an initial condition different to the one observed—for example, in the last available data point of historical data—it is possible to rescale the simulation data such that the simulation’s intertemporal variation reflects the initial conditions of a particular emissions trajectory more accurately.

<figure><img src="image_71.png" /><figcaption>Figure 5. Point estimate and standard deviation of estimated parameters.</figcaption></figure>

## Contrast with Other Modeling Approaches

SiSePuede takes a unique, sectoral transformation-centered approach to modeling emissions, contrasting with other common modeling techniques. For example, integrated assessment models (IAMs) are used to model biophysical emissions processes as driven by anthropogenic activity, and some include biophysical feedback. In accordance with IPCC inventory guidelines, SiSePuede bases emissions on emission factors that respond to drivers, though certain phenomena—such as methane emissions from anaerobic decomposition in solid waste landfills, which are modeled using a first-order decay model—include biophysical treatments. Biophysical feedback such as impacts of climate change on hydropower production or crop yield factors are treated as uncertain factors that can be explored within reasonable expected ranges.
Given their computational requirements, IAMs require significant computational power for evaluating portfolios of specific policy transformations and sectoral actions because they are often massive and chaotic, requiring extensive time and computational power to assess a single scenario. The relative numerical simplicity of calculations in SiSePuede and the integrated uncertainty framework allow extensive exploration over uncertainties at highly refined levels, facilitating a robust exploration of strategies. SiSePuede endogenizes uncertainty exploration through Latin Hypercube sampling across exogenous uncertainties and intervention effects, a robust data management system, and scalable computational architecture.

SiSePuede also differs significantly from general- and partial-equilibrium models (PEMs and GEMs). Equilibrium models, which endogenize economic outcomes based on exogenous factors such as prices, are another class of models paired with emission factors to estimate how changes in economic activity may drive emissions changes. However, while GEMs advantageously endogenize demands, they can be extremely difficult to calibrate and solve, especially when exploring large parameter and variable spaces, such as supplies and demands for a refined range of products and services across an entire economy. While SiSePuede does not endogenize supplies and demands through market-clearing conditions, it does facilitate exploration over combinations of prices and behaviors that then can be used to identify policy-relevant scenarios across a range of potential futures.

SiSePuede relies on some key assumptions to deal with potential conflicts that may arise from the use of exogenous variable specifications. Most notably, consider the interaction between agriculture and livestock demands and land use. Demand for crops and animal products are modeled endogenously as a combination of historical demand, import fractions, exogenous exports, red meat consumption behavior, changes to productivity, and elasticities to GDP and GDP/capita (used as an endogenous proxy for average income). Land-use transition probabilities also are defined exogenously in the model, representing expectations for region's future land use. Because cattle and cropland drive extensive shifts in land use, these two trajectories can come into conflict if left unresolved.

To reconcile differences in exogenous specifications of demands for land use, SiSePuede introduces a novel parameter referred to as the land-use reallocation factor. The land-use reallocation factor represents the fraction of land needed (or not needed) for crop and livestock production that is adjusted to ensure land use meets demands. In the initial state, demands for crop and livestock production are combined with land-use prevalence to determine a baseline carrying capacity for grazing livestock. As livestock stock demands change, baseline carrying capacities—which can be scaled up or down to represent changes to productivity—are used to estimate land-use requirements needed to fulfill these domestic production demands. If demands for crops or livestock increase, then more land is required. Alternatively, if livestock demands decrease or carrying capacities increase, more land may be available for restoration or reforestation, critical components of decarbonization pathways.
Given their computational requirements, IAMs require significant computational power for evaluating portfolios of specific policy transformations and sectoral actions because they are often massive and chaotic, requiring extensive time and computational power to assess a single scenario. The relative numerical simplicity of calculations in SiSePuede and the integrated uncertainty framework allow extensive exploration over uncertainties at highly refined levels, facilitating a robust exploration of strategies. SiSePuede endogenizes uncertainty exploration through Latin Hypercube sampling across exogenous uncertainties and intervention effects, a robust data management system, and scalable computational architecture.

SiSePuede also differs significantly from general- and partial-equilibrium models (PEMs and GEMs). Equilibrium models, which endogenize economic outcomes based on exogenous factors such as prices, are another class of models paired with emission factors to estimate how changes in economic activity may drive emissions changes. However, while GEMs advantageously endogenize demands, they can be extremely difficult to calibrate and solve, especially when exploring large parameter and variable spaces, such as supplies and demands for a refined range of products and services across an entire economy. While SiSePuede does not endogenize supplies and demands through market-clearing conditions, it does facilitate exploration over combinations of prices and behaviors that then can be used to identify policy-relevant scenarios across a range of potential futures.

SiSePuede relies on some key assumptions to deal with potential conflicts that may arise from the use of exogenous variable specifications. Most notably, consider the interaction between agriculture and livestock demands and land use. Demand for crops and animal products are modeled endogenously as a combination of historical demand, import fractions, exogenous exports, red meat consumption behavior, changes to productivity, and elasticities to GDP and GDP/capita (used as an endogenous proxy for average income). Land-use transition probabilities also are defined exogenously in the model, representing expectations for region's future land use. Because cattle and cropland drive extensive shifts in land use, these two trajectories can come into conflict if left unresolved.

To reconcile differences in exogenous specifications of demands for land use, SiSePuede introduces a novel parameter referred to as the land-use reallocation factor. The land-use reallocation factor represents the fraction of land needed (or not needed) for crop and livestock production that is adjusted to ensure land use meets demands. In the initial state, demands for crop and livestock production are combined with land-use prevalence to determine a baseline carrying capacity for grazing livestock. As livestock stock demands change, baseline carrying capacities—which can be scaled up or down to represent changes to productivity—are used to estimate land-use requirements needed to fulfill these domestic production demands. If demands for crops or livestock increase, then more land is required. Alternatively, if livestock demands decrease or carrying capacities increase, more land may be available for restoration or reforestation, critical components of decarbonization pathways.
The land-use reallocation factor determines the fraction of land-use deficit, or how much is needed less how much is specified, that is reallocated away from exogenous transition trajectories to meet demands for grazeland. Using this value, columns on the land-use transition matrix1 are scaled accordingly to ensure transitions into grazeland meet demands. If the factor is 0, then no land use is reallocated away from exogenous land-use transition trajectories, and all livestock demands that exceed carrying capacity are used to adjust exports, while deficits are met with new imports. If the factor is set to 1, then transition probabilities are scaled to ensure that pastures and croplands will meet production demands precisely. Any value in-between is the fraction of land that is moved away from exogenous specification through column scaling, representing a mix between the two approaches.

## Accessing SiSePuede

The complete documentation for SiSePuede—including the installation instructions, detailed mathematical specifications, variable information, and schema—are available at https://sisepuede.readthedocs.io. SiSePuede is written in Python and Julia. NemoMod was developed by Stockholm Environmental Institute and is available under the Apache 2.0 License from https://github.com/sei-international/NemoMod.jl. Python and Julia model code are available for use under the GNU Public License at https://github.com/jcsyme/sisepuede, and a precompiled Docker image can be used to run the latest version of the model, available at https://hub.docker.com/r/jsyme816/sisepuede.

<footnote n="1">¹ SiSePuede treats the land-use transition matrix as a row-stochastic Markov chain.</footnote>
The land-use reallocation factor determines the fraction of land-use deficit, or how much is needed less how much is specified, that is reallocated away from exogenous transition trajectories to meet demands for grazeland. Using this value, columns on the land-use transition matrix1 are scaled accordingly to ensure transitions into grazeland meet demands. If the factor is 0, then no land use is reallocated away from exogenous land-use transition trajectories, and all livestock demands that exceed carrying capacity are used to adjust exports, while deficits are met with new imports. If the factor is set to 1, then transition probabilities are scaled to ensure that pastures and croplands will meet production demands precisely. Any value in-between is the fraction of land that is moved away from exogenous specification through column scaling, representing a mix between the two approaches.

## Accessing SiSePuede

The complete documentation for SiSePuede—including the installation instructions, detailed mathematical specifications, variable information, and schema—are available at https://sisepuede.readthedocs.io. SiSePuede is written in Python and Julia. NemoMod was developed by Stockholm Environmental Institute and is available under the Apache 2.0 License from https://github.com/sei-international/NemoMod.jl. Python and Julia model code are available for use under the GNU Public License at https://github.com/jcsyme/sisepuede, and a precompiled Docker image can be used to run the latest version of the model, available at https://hub.docker.com/r/jsyme816/sisepuede.

<footnote n="1">¹ SiSePuede treats the land-use transition matrix as a row-stochastic Markov chain.</footnote>
# Appendix A. Agriculture

The agricultural sector consists of crop and livestock production. The demand for crops and livestock is driven by population and GDP growth. Crops and livestock are distinguished by type according to FAO categories. We discuss crop and livestock production separately.

## Crops

### Crop historical data and projections

Emissions in crops are produced by the release of soil carbon in tillage, fertilizer applications and crop liming, crop burning, organic material's decomposition, and methane emissions from paddy rice fields. Fossil fuels burned by on-farm equipment are accounted for in industrial energy, where demands for energy are driven by agriculture and livestock production. Agriculture emissions are a product of the data in Table A.1.

<table><caption>Table A.1 Data and methods</caption><thead><tr><td>Data</td><td>Method for historical data</td><td>Method for projections</td></tr></thead><tbody><tr><td>Quantity of agricultural demand per agricultural category per country</td><td>Production volumes are estimated using the United Nations Food and Agriculture Organization (FAO) Production database (FAO, 2023c)<br/>Exports and import volumes are estimated using the FAO Trade database (FAO, 2023d)</td><td>We use the US Department of Agriculture (USDA) Commodity and Food Elasticities Database for estimating crop demand income elasticities by country. We combine these elasticities with gross domestic product (GDP) per capita projections to estimate baseline demand.</td></tr><tr><td>Yield (land area and fertilizer and other inputs needed per unit of crop output)</td><td>Yields and area harvested figures are estimated using FAO Production database (FAO, 2023c)<br/>Fertilizer use is estimated based on data from the International Fertilizer Association (IFA, 2023)</td><td>Yields are projected to grow 1.6% per year from 2020 to 2050.<br/>Areas and volumes of production, exports, and imports are determined endogenously in the simulation.</td></tr><tr><td>Emissions intensity (emissions per ton of crop produced)</td><td>Fertilizer use emission factors based on Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories, Table 11.1, volume 4 (IPPC, 2019)</td><td>Factors are assumed to remain constant under baseline conditions (i.e., no transformations).</td></tr><tr><td>Total emissions</td><td>Historical sector emissions are used in the calibration process. Emissions data for calibration are obtained from Climate Watch (2022)</td><td>Production of different crops and technologies and practices used in agriculture determine emissions levels.</td></tr></tbody></table>

Note: To map SiSePuede crops categories to FAO crops categories, we developed data crosswalks that allow us to aggregate statistical information from FAO to be used as input in SiSePuede.
# Appendix A. Agriculture

The agricultural sector consists of crop and livestock production. The demand for crops and livestock is driven by population and GDP growth. Crops and livestock are distinguished by type according to FAO categories. We discuss crop and livestock production separately.

## Crops

### Crop historical data and projections

Emissions in crops are produced by the release of soil carbon in tillage, fertilizer applications and crop liming, crop burning, organic material's decomposition, and methane emissions from paddy rice fields. Fossil fuels burned by on-farm equipment are accounted for in industrial energy, where demands for energy are driven by agriculture and livestock production. Agriculture emissions are a product of the data in Table A.1.

<table><caption>Table A.1 Data and methods</caption><thead><tr><td>Data</td><td>Method for historical data</td><td>Method for projections</td></tr></thead><tbody><tr><td>Quantity of agricultural demand per agricultural category per country</td><td>Production volumes are estimated using the United Nations Food and Agriculture Organization (FAO) Production database (FAO, 2023c)<br>Exports and import volumes are estimated using the FAO Trade database (FAO, 2023d)</td><td>We use the US Department of Agriculture (USDA) Commodity and Food Elasticities Database for estimating crop demand income elasticities by country. We combine these elasticities with gross domestic product (GDP) per capita projections to estimate baseline demand.</td></tr><tr><td>Yield (land area and fertilizer and other inputs needed per unit of crop output)</td><td>Yields and area harvested figures are estimated using FAO Production database (FAO, 2023c)<br>Fertilizer use is estimated based on data from the International Fertilizer Association (IFA, 2023)</td><td>Yields are projected to grow 1.6% per year from 2020 to 2050.<br>Areas and volumes of production, exports, and imports are determined endogenously in the simulation.</td></tr><tr><td>Emissions intensity (emissions per ton of crop produced)</td><td>Fertilizer use emission factors based on Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories, Table 11.1, volume 4 (IPPC, 2019)</td><td>Factors are assumed to remain constant under baseline conditions (i.e., no transformations).</td></tr><tr><td>Total emissions</td><td>Historical sector emissions are used in the calibration process. Emissions data for calibration are obtained from Climate Watch (2022)</td><td>Production of different crops and technologies and practices used in agriculture determine emissions levels.</td></tr></tbody></table>

Note: To map SiSePuede crops categories to FAO crops categories, we developed data crosswalks that allow us to aggregate statistical information from FAO to be used as input in SiSePuede.
Transforming crops

While many individual practices can address emissions from crop production, we broadly group practices into three transformations: improving the use of fertilizers, expanding conservation agriculture, and improving rice management, consistent with strategies from the Environmental Protection Agency or EPA (2019) and McKinsey (2019 & 2020) that are non-duplicative and have significant applicability in Latin America and the Caribbean (LAC). The fourth, discussed in the section “Crosscutting Changes in Agriculture” is to increase overall sector productivity, in ways that do not specifically target emissions.

Table A.2 shows transformations to reduce emissions from crops. Table A.3 shows the technical costs and benefits of these transformations, and Table A.4 shows the sector-specific non-technical costs and benefits. The costs and benefits of transforming agriculture on energy and waste management are valued endogenously in those sectors.

<table><caption>Table A.2 Crop transformations</caption><thead><tr><td>Transformation</td><td>Description</td><td>Implementation (by 2050)</td></tr></thead><tbody><tr><td>Reduce excess fertilizer use</td><td>Use of fertilizer in agriculture is the primary source of anthropogenic nitrous oxide (N2O) emissions (Tian et al., 2020). Global overuse and misapplication of N2O is a significant and avoidable contributor to these emissions (McKinsey et al., 2020). This strategy targets applying excess nitrogen (i.e., the amount of fertilizer that can be reduced without affecting yield), which is highest in countries such as China and India that subsidize fertilizer use but is also significant in Latin America (West et al., 2017). Table X.2 specifies per-hectare nitrogen (N) input that is not taken up by harvested crops, of which 10-30% could be avoided without an impact on yield.</td><td>Any fertilizer historically applied beyond the benefits it yields is avoided.</td></tr></tbody></table>
Transforming crops

While many individual practices can address emissions from crop production, we broadly group practices into three transformations: improving the use of fertilizers, expanding conservation agriculture, and improving rice management, consistent with strategies from the Environmental Protection Agency or EPA (2019) and McKinsey (2019 & 2020) that are non-duplicative and have significant applicability in Latin America and the Caribbean (LAC). The fourth, discussed in the section “Crosscutting Changes in Agriculture” is to increase overall sector productivity, in ways that do not specifically target emissions.

Table A.2 shows transformations to reduce emissions from crops. Table A.3 shows the technical costs and benefits of these transformations, and Table A.4 shows the sector-specific non-technical costs and benefits. The costs and benefits of transforming agriculture on energy and waste management are valued endogenously in those sectors.

<table><caption>Table A.2 Crop transformations</caption><thead><tr><td>Transformation</td><td>Description</td><td>Implementation (by 2050)</td></tr></thead><tbody><tr><td>Reduce excess fertilizer use</td><td>Use of fertilizer in agriculture is the primary source of anthropogenic nitrous oxide (N2O) emissions (Tian et al., 2020). Global overuse and misapplication of N2O is a significant and avoidable contributor to these emissions (McKinsey et al., 2020). This strategy targets applying excess nitrogen (i.e., the amount of fertilizer that can be reduced without affecting yield), which is highest in countries such as China and India that subsidize fertilizer use but is also significant in Latin America (West et al., 2017). Table X.2 specifies per-hectare nitrogen (N) input that is not taken up by harvested crops, of which 10-30% could be avoided without an impact on yield.</td><td>Any fertilizer historically applied beyond the benefits it yields is avoided.</td></tr></tbody></table>
<table><tr><td>Expand conservation agriculture</td><td>Conservation agriculture is the term given to agricultural practices that seek to preserve soil and ecosystem health. FAO describes three inter-related practices: minimum tilling, maintaining permanent soil cover, and diversifying plant species (FAO, 2022). While Latin America is already a leader in no-till practices (McKinsey et al., 2020; Kassam et al., 2019; Sperow, 2020), there is room for improving these practices' extent and scope, particularly given the poor quality of some current efforts (Kassam, 2019).</td><td>High-quality conservation agricultural practices are expanded to 80% of crop land on which grains and staples are grown, excluding rice (which is addressed in the next transformation).</td></tr><tr><td>Improve rice management</td><td>Many practices can reduce emissions associated with growing rice, including improved water management, fertilizer practices, tillage practices, rice variety choices, residue management, and seeding practices (McKinsey et al., 2020; Chirinda et al., 2018).</td><td>All rice growing is transitioned to these improved practices.</td></tr><tr><td>Improve sector productivity</td><td>This encompasses many sector-wide improvements that increase productivity.</td><td>This is described in the section "Crosscutting Agriculture Changes."</td></tr></table>

Notes: West et al. (2017) estimate the excess N per hectare (ha) per country globally (measured as the difference between rates of nutrient input versus nutrient removal from plant harvesting). They estimate that 10-30% could be removed without impact on yield. Data are available for download at ; accessed May 25, 2022).
<table><tr><td>Expand conservation agriculture</td><td>Conservation agriculture is the term given to agricultural practices that seek to preserve soil and ecosystem health. FAO describes three inter-related practices: minimum tilling, maintaining permanent soil cover, and diversifying plant species (FAO, 2022). While Latin America is already a leader in no-till practices (McKinsey et al., 2020; Kassam et al., 2019; Sperow, 2020), there is room for improving these practices' extent and scope, particularly given the poor quality of some current efforts (Kassam, 2019).</td><td>High-quality conservation agricultural practices are expanded to 80% of crop land on which grains and staples are grown, excluding rice (which is addressed in the next transformation).</td></tr><tr><td>Improve rice management</td><td>Many practices can reduce emissions associated with growing rice, including improved water management, fertilizer practices, tillage practices, rice variety choices, residue management, and seeding practices (McKinsey et al., 2020; Chirinda et al., 2018).</td><td>All rice growing is transitioned to these improved practices.</td></tr><tr><td>Improve sector productivity</td><td>This encompasses many sector-wide improvements that increase productivity.</td><td>This is described in the section "Crosscutting Agriculture Changes."</td></tr></table>

Notes: West et al. (2017) estimate the excess N per hectare (ha) per country globally (measured as the difference between rates of nutrient input versus nutrient removal from plant harvesting). They estimate that 10-30% could be removed without impact on yield. Data are available for download at ; accessed May 25, 2022).
<table><caption>Table A.3 Crop transformations' technical costs</caption><thead><tr><th>Transformation</th><th>Technical costs<br>(Labor cost average in 2019<br>USD)<br>(Negative values indicate avoided<br>costs, i.e., savings)</th><th>Notes and data</th></tr></thead><tbody><tr><td>Reduce excess fertilizer use</td><td>-$200/ton of fertilizer</td><td>West (2017), Good and Beatty (2011). We calculate the cost of eliminating excess N as the avoided cost of fertilizer using the minimum, average, and maximum costs per ton of urea over the past five years available for download, accessed June 17, 2023.</td></tr><tr><td>Expand conservation agriculture</td><td>-$20/ha under conservation agriculture</td><td>Savings from avoided fuel and labor expenditures make this transformation a technical cost savings. Crop-specific estimates include savings of $232/ha for soy and $84/ha for maize (SHP, EDF, and Isom [2021]), and general savings estimated at $23/ha (Frank et al., 2018).</td></tr><tr><td>Improve rice management</td><td>-$30/ha under improved rice management</td><td>McKinsey (2020) describes various rice-management practices and techniques that can yield significant carbon reductions, all at negative technical cost. Chirinda et al.'s (2018) metanalysis of rice management describes impacts on greenhouse gases (GHGs) and yields in Latin America for a variety of these practices, and we use that data to inform the ranges. Chakraborty et al. (2017, Supplementary Table S8) describes the difference in costs for these practices globally, which we adapt here, adjusting in dollar values from 2017 to 2019.</td></tr></tbody></table>
<table><caption>Table A.3 Crop transformations' technical costs</caption><thead><tr><th>Transformation</th><th>Technical costs<br>(Labor cost average in 2019<br>USD)<br>(Negative values indicate avoided<br>costs, i.e., savings)</th><th>Notes and data</th></tr></thead><tbody><tr><td>Reduce excess fertilizer use</td><td>-$200/ton of fertilizer</td><td>West (2017), Good and Beatty (2011). We calculate the cost of eliminating excess N as the avoided cost of fertilizer using the minimum, average, and maximum costs per ton of urea over the past five years available for download, accessed June 17, 2023.</td></tr><tr><td>Expand conservation agriculture</td><td>-$20/ha under conservation agriculture</td><td>Savings from avoided fuel and labor expenditures make this transformation a technical cost savings. Crop-specific estimates include savings of $232/ha for soy and $84/ha for maize (SHP, EDF, and Isom [2021]), and general savings estimated at $23/ha (Frank et al., 2018).</td></tr><tr><td>Improve rice management</td><td>-$30/ha under improved rice management</td><td>McKinsey (2020) describes various rice-management practices and techniques that can yield significant carbon reductions, all at negative technical cost. Chirinda et al.'s (2018) metanalysis of rice management describes impacts on greenhouse gases (GHGs) and yields in Latin America for a variety of these practices, and we use that data to inform the ranges. Chakraborty et al. (2017, Supplementary Table S8) describes the difference in costs for these practices globally, which we adapt here, adjusting in dollar values from 2017 to 2019.</td></tr></tbody></table>
<table><caption>Table A.4 Benefits and costs of transforming agriculture</caption><thead><tr><td>Benefit or cost</td><td>Value (LAC average in 2019 USD) (Positive indicates benefits)</td><td>Notes and data sources</td></tr></thead><tbody><tr><td>Avoided nitrate leaching and runoff</td><td>$60/ton of fertilizer avoided</td><td>Good and Beatty (2011, Box 2 and Table 4) estimate the total environmental cost of nitrate leaching and runoff to be 44% of the total cost of excess fertilizer applied, of which 70% of is attributed to nitrate leaching and runoff. The value of avoided nitrate leaching and runoff is estimated at 30% of the per-ton cost of fertilizer in the prior table.</td></tr><tr><td>Improved soil health</td><td>$350/ha</td><td>Telles et al. (2018) estimate the difference in agricultural land values in Brazil in 2006 under different tillage practices. They find that on average, hectares of no-till are over $700 more valuable per acre than those under conventional tillage practices, which can serve as a proxy for the benefits of healthier soils from conservation tillage. Recognizing that agricultural land values vary greatly by country and that the causal direction of the relationship between value of land and tillage practice may be complex, we use a conservative estimate of the difference to value the benefits of this practice.</td></tr></tbody></table>

Note: LAC stands for Latin America and the Caribbean.
<table><caption>Table A.4 Benefits and costs of transforming agriculture</caption><thead><tr><th>Benefit or cost</th><th>Value (LAC average in 2019 USD)<br>(Positive indicates benefits)</th><th>Notes and data sources</th></tr></thead><tbody><tr><td>Avoided nitrate leaching and runoff</td><td>$60/ton of fertilizer avoided</td><td>Good and Beatty (2011, Box 2 and Table 4) estimate the total environmental cost of nitrate leaching and runoff to be 44% of the total cost of excess fertilizer applied, of which 70% of is attributed to nitrate leaching and runoff. The value of avoided nitrate leaching and runoff is estimated at 30% of the per-ton cost of fertilizer in the prior table.</td></tr><tr><td>Improved soil health</td><td>$350/ha</td><td>Telles et al. (2018) estimate the difference in agricultural land values in Brazil in 2006 under different tillage practices. They find that on average, hectares of no-till are over $700 more valuable per acre than those under conventional tillage practices, which can serve as a proxy for the benefits of healthier soils from conservation tillage. Recognizing that agricultural land values vary greatly by country and that the causal direction of the relationship between value of land and tillage practice may be complex, we use a conservative estimate of the difference to value the benefits of this practice.</td></tr></tbody></table>

Note: LAC stands for Latin America and the Caribbean.
# Livestock

## Livestock historical data and projections

Emissions in livestock are produced by enteric fermentation (for ruminants), manure, and converting land to pasture, which we discuss in the land use and forests sector. Livestock emissions are a product of the data in Table A.5.

<table><caption>**Table A.5 Data and methods**</caption><thead><tr><td>Data</td><td>Method For historical data</td><td>Method for projections</td></tr></thead><tbody><tr><td>Initial livestock head count</td><td>Live animals head count is estimated using UN FAO Production database (FAO, 2023c)<br/>Export and import volumes are estimated using FAO Trade database (FAO, 2023d)</td><td>We estimate livestock demand's elasticity to GDP per capita based on data from Komarek et al. (2021). We combine these elasticities with GDP per capita projections to estimate baseline demand.</td></tr><tr><td>Dry matter consumption</td><td>Daily dry matter consumption is taken from Holechek (1988)</td><td>Dry matter consumption is assumed constant through the simulation. However, areas and volumes of production, exports, and imports are determined endogenously in the simulation.</td></tr><tr><td>Emissions intensity</td><td>Enteric fermentation factors' values are taken from IPCC Guidelines for National Greenhouse Gas Inventories, Tables 10.10-10.11, volume 4, chapter 10 (IPPC, 2019)<br/>Livestock manure management fractions are taken from IPCC Guidelines for National Greenhouse Gas Inventories, Table 10A.6, volume 4, chapter 10 (IPPC, 2019)</td><td>Factors are assumed to remain constant under baseline conditions (i.e., no transformations).</td></tr><tr><td>Total emissions</td><td>Historical sector emissions are used in the calibration process. Emissions data for calibration are obtained from Climate Watch (2022)</td><td>Meat demand, along with the technologies and practices used for production, determine emission levels in the future.</td></tr></tbody></table>

Notes: Export data contain records on processed animal products. We developed a crosswalk to convert these export statistics to equivalent animal head counts. Daily dry matter consumption is used to allocate grassland to grazing animals and estimate carrying capacity under the assumption that livestock's distribution across grazelands is uniform, grasslands are homogeneous, and there is no mixed grazing. Climate Watch data aggregate crops and livestock into one single sector named "agriculture." We approximate livestock emissions using CH4 totals produced by Climate Watch.
# Livestock

## Livestock historical data and projections

Emissions in livestock are produced by enteric fermentation (for ruminants), manure, and converting land to pasture, which we discuss in the land use and forests sector. Livestock emissions are a product of the data in Table A.5.

<table><caption>**Table A.5 Data and methods**</caption><thead><tr><td>Data</td><td>Method For historical data</td><td>Method for projections</td></tr></thead><tbody><tr><td>Initial livestock head count</td><td>Live animals head count is estimated using UN FAO Production database (FAO, 2023c)<br/>Export and import volumes are estimated using FAO Trade database (FAO, 2023d)</td><td>We estimate livestock demand's elasticity to GDP per capita based on data from Komarek et al. (2021). We combine these elasticities with GDP per capita projections to estimate baseline demand.</td></tr><tr><td>Dry matter consumption</td><td>Daily dry matter consumption is taken from Holechek (1988)</td><td>Dry matter consumption is assumed constant through the simulation. However, areas and volumes of production, exports, and imports are determined endogenously in the simulation.</td></tr><tr><td>Emissions intensity</td><td>Enteric fermentation factors' values are taken from IPCC Guidelines for National Greenhouse Gas Inventories, Tables 10.10-10.11, volume 4, chapter 10 (IPPC, 2019)<br/>Livestock manure management fractions are taken from IPCC Guidelines for National Greenhouse Gas Inventories, Table 10A.6, volume 4, chapter 10 (IPPC, 2019)</td><td>Factors are assumed to remain constant under baseline conditions (i.e., no transformations).</td></tr><tr><td>Total emissions</td><td>Historical sector emissions are used in the calibration process. Emissions data for calibration are obtained from Climate Watch (2022)</td><td>Meat demand, along with the technologies and practices used for production, determine emission levels in the future.</td></tr></tbody></table>

Notes: Export data contain records on processed animal products. We developed a crosswalk to convert these export statistics to equivalent animal head counts. Daily dry matter consumption is used to allocate grassland to grazing animals and estimate carrying capacity under the assumption that livestock's distribution across grazelands is uniform, grasslands are homogeneous, and there is no mixed grazing. Climate Watch data aggregate crops and livestock into one single sector named "agriculture." We approximate livestock emissions using CH4 totals produced by Climate Watch.
<table><caption>Table A.7 Livestock transformations' technical costs</caption><thead><tr><td>Transformation</td><td>Cost</td><td>Source</td></tr></thead><tbody><tr><td>Reducing enteric fermentation</td><td>$40/head</td><td>The cost of reduced enteric fermentation is taken as the range of costs in Table 5-59 in EPA (2019) for fermentation inhibitors, and the nominal value is the average of that range. McKinsey (2020) reports breed change as a no-cost transformation. The costs of enteric fermentation inhibitors are converted from 2010 to 2019 dollars and adjusted to LAC values.</td></tr><tr><td>Managing manure</td><td>$10/TLU</td><td>Frank et al. (2018) suggest global costs of digesters from $8-$38/ Tropical livestock units (TLU). We use an average for digesters and then adjust to account for the fact that much of the manure in this transformation is handled by lower-cost methods such as spreading on fields.</td></tr><tr><td>Silvopasture</td><td>$45/ha/year</td><td>We conservatively estimate silvopasture's cost as the same as the cost of restoring degraded land, although this may be a higher cost than is seen in the literature.</td></tr></tbody></table>

## Crosscutting Agriculture Supply-and-Demand Transformations

Several transformations shape the underlying supply, demand, and productivity of the agricultural system and can have significant impacts on emissions. They include shifting consumer diets to reduce meat consumption, reducing food losses and waste, improving productivity, and changing agricultural land-use policy.

Table A.8 shows these transformations, and Table A.9 shows their technical costs and benefits. Table A.10 shows the sector-specific non-technical costs and benefits. The costs and benefits of these transformations on land use are described in the land use and forestry sector.
<table><caption>Table A.7 Livestock transformations' technical costs</caption><thead><tr><td>Transformation</td><td>Cost</td><td>Source</td></tr></thead><tbody><tr><td>Reducing enteric fermentation</td><td>$40/head</td><td>The cost of reduced enteric fermentation is taken as the range of costs in Table 5-59 in EPA (2019) for fermentation inhibitors, and the nominal value is the average of that range. McKinsey (2020) reports breed change as a no-cost transformation. The costs of enteric fermentation inhibitors are converted from 2010 to 2019 dollars and adjusted to LAC values.</td></tr><tr><td>Managing manure</td><td>$10/TLU</td><td>Frank et al. (2018) suggest global costs of digesters from $8-$38/ Tropical livestock units (TLU). We use an average for digesters and then adjust to account for the fact that much of the manure in this transformation is handled by lower-cost methods such as spreading on fields.</td></tr><tr><td>Silvopasture</td><td>$45/ha/year</td><td>We conservatively estimate silvopasture's cost as the same as the cost of restoring degraded land, although this may be a higher cost than is seen in the literature.</td></tr></tbody></table>

## Crosscutting Agriculture Supply-and-Demand Transformations

Several transformations shape the underlying supply, demand, and productivity of the agricultural system and can have significant impacts on emissions. They include shifting consumer diets to reduce meat consumption, reducing food losses and waste, improving productivity, and changing agricultural land-use policy.

Table A.8 shows these transformations, and Table A.9 shows their technical costs and benefits. Table A.10 shows the sector-specific non-technical costs and benefits. The costs and benefits of these transformations on land use are described in the land use and forestry sector.
<table><caption>Table A.8 Crosscutting transformations</caption><thead><tr><td>Transformation</td><td>Description</td><td>Implementation (by 2050)</td></tr></thead><tbody><tr><td>Improving diets</td><td>Latin America has high rates of obesity and poor nutrition, with attendant social health costs (Popkin and Reardon, 2018). This transformation transitions the population in aggregate to a healthier and more sustainable diet consistent with the vegetarian diet described in Table S.7 in Springmann et al. (2016).</td><td>By 2040, overall dietary consumption has shifted in a manner that—in aggregate—is consistent with 40% of the population adopting a vegetarian diet, although the distribution of dietary change across the population will vary. Some will forego meat, others will reduce their consumption, and still others who have inadequate access to protein will increase it.</td></tr><tr><td>Reducing food losses and waste in the supply chain</td><td>Food waste and loss is a massive economic cost to LAC and globally (Hanson et al., 2022; Flanagan et al., 2019). As of 2020, an average of 12% of food produced on the farm in LAC is lost before it reaches retailers (FAO, undated). This is approximately 171 kg per capita (UNEP, 2018). This strategy reduces food waste throughout the agriculture supply chain, from the farm through production, processing, handling and storage, and distribution and marketing. (Post-market waste by consumers is handled in the waste sector.)</td><td>The maximum implementation of this transformation involves halving food losses in the supply chain, consistent with Hanson et al.'s (20220) recommendations.</td></tr><tr><td>Improving agricultural and livestock productivity</td><td>Latin America is not yet at the frontier of agricultural productivity. This transformation accelerates total factor productivity in Latin America to Organization for Economic Co-operation and Development (OECD) levels.</td><td>Agricultural productivity will grow 1.6% per year throughout 2050</td></tr><tr><td>Redirecting gains in productivity to land conservation</td><td>As agricultural or livestock productivity increases, or domestic demand decreases, domestic production can be reduced and land returned to native conditions.</td><td>All gains in productivity or decreases in waste or demand are used to reduce production (rather than export more).</td></tr></tbody></table>
<table><caption>Table A.8 Crosscutting transformations</caption><thead><tr><td>Transformation</td><td>Description</td><td>Implementation (by 2050)</td></tr></thead><tbody><tr><td>Improving diets</td><td>Latin America has high rates of obesity and poor nutrition, with attendant social health costs (Popkin and Reardon, 2018). This transformation transitions the population in aggregate to a healthier and more sustainable diet consistent with the vegetarian diet described in Table S.7 in Springmann et al. (2016).</td><td>By 2040, overall dietary consumption has shifted in a manner that—in aggregate—is consistent with 40% of the population adopting a vegetarian diet, although the distribution of dietary change across the population will vary. Some will forego meat, others will reduce their consumption, and still others who have inadequate access to protein will increase it.</td></tr><tr><td>Reducing food losses and waste in the supply chain</td><td>Food waste and loss is a massive economic cost to LAC and globally (Hanson et al., 2022; Flanagan et al., 2019). As of 2020, an average of 12% of food produced on the farm in LAC is lost before it reaches retailers (FAO, undated). This is approximately 171 kg per capita (UNEP, 2018). This strategy reduces food waste throughout the agriculture supply chain, from the farm through production, processing, handling and storage, and distribution and marketing. (Post-market waste by consumers is handled in the waste sector.)</td><td>The maximum implementation of this transformation involves halving food losses in the supply chain, consistent with Hanson et al.'s (20220) recommendations.</td></tr><tr><td>Improving agricultural and livestock productivity</td><td>Latin America is not yet at the frontier of agricultural productivity. This transformation accelerates total factor productivity in Latin America to Organization for Economic Co-operation and Development (OECD) levels.</td><td>Agricultural productivity will grow 1.6% per year throughout 2050</td></tr><tr><td>Redirecting gains in productivity to land conservation</td><td>As agricultural or livestock productivity increases, or domestic demand decreases, domestic production can be reduced and land returned to native conditions.</td><td>All gains in productivity or decreases in waste or demand are used to reduce production (rather than export more).</td></tr></tbody></table>
<table><caption>Table A.9 Crosscutting transformations' technical costs</caption><thead><tr><th>Transformation</th><th>Technical costs (LAC average in 2019 USD)<br>(Negative values indicate avoided costs, i.e., savings)</th><th>Notes and data sources</th></tr></thead><tbody><tr><td>Reducing supply chain losses and waste</td><td>$400/ton of food waste avoided</td><td>Costs are based on the average per-ton costs of methods of reducing producer-side food waste in the United States, weighted by their effect size ($700 in 2016), based on data found in the appendix of ReFED (2016), adjusted to LAC. We expect this is an overestimate of costs, given that Latin America's food industry is generally less well developed than the United States, and gains may be available at a lower cost.</td></tr><tr><td></td><td>-$500/ton of food waste avoided</td><td>Food waste occurs across food types, and without specific information on the types of food that are wasted and the recovery potential in the supply chain, we use the average cost of food across all product types. We reduce this value given that the food that is recovered from waste may be of lower value than food that is not wasted.</td></tr><tr><td>Increasing crop or livestock productivity</td><td>Varies by country</td><td>This is deeply uncertain. As an initial estimate, we use the average annual investment in agriculture and livestock research and development (R&D) in OECD countries, as a fraction of GDP (roughly 0.02%). We use that fraction for OECD countries in LAC; for others, we increase that fraction by 20% to account for less-developed economies.</td></tr><tr><td>Change in sector value add</td><td>Price per ton varies by type of product</td><td>The value per ton of crops and livestock varies by type and is based on producer prices from FAOStat's food producer prices dataset (FAO, undated), with TLU values (FAO, 2023) and average production efficiencies for livestock (Williams and Anderson, 2020).</td></tr></tbody></table>
<table><caption>Table A.9 Crosscutting transformations' technical costs</caption><thead><tr><th>Transformation</th><th>Technical costs (LAC average in 2019 USD)<br>(Negative values indicate avoided costs, i.e., savings)</th><th>Notes and data sources</th></tr></thead><tbody><tr><td rowspan="2">Reducing supply chain losses and waste</td><td>$400/ton of food waste avoided</td><td>Costs are based on the average per-ton costs of methods of reducing producer-side food waste in the United States, weighted by their effect size ($700 in 2016), based on data found in the appendix of ReFED (2016), adjusted to LAC. We expect this is an overestimate of costs, given that Latin America's food industry is generally less well developed than the United States, and gains may be available at a lower cost.</td></tr><tr><td>-$500/ton of food waste avoided</td><td>Food waste occurs across food types, and without specific information on the types of food that are wasted and the recovery potential in the supply chain, we use the average cost of food across all product types. We reduce this value given that the food that is recovered from waste may be of lower value than food that is not wasted.</td></tr><tr><td>Increasing crop or livestock productivity</td><td>Varies by country</td><td>This is deeply uncertain. As an initial estimate, we use the average annual investment in agriculture and livestock research and development (R&D) in OECD countries, as a fraction of GDP (roughly 0.02%). We use that fraction for OECD countries in LAC; for others, we increase that fraction by 20% to account for less-developed economies.</td></tr><tr><td>Change in sector value add</td><td>Price per ton varies by type of product</td><td>The value per ton of crops and livestock varies by type and is based on producer prices from FAOStat's food producer prices dataset (FAO, undated), with TLU values (FAO, 2023) and average production efficiencies for livestock (Williams and Anderson, 2020).</td></tr></tbody></table>
<table><caption>Table A.10 Crosscutting agricultural transformations' non-technical benefits and costs</caption><thead><tr><td>Value</td><td>Value (LAC average in 2019 USD) (Positive indicates benefits)</td><td>Notes and data sources</td></tr></thead><tbody><tr><td>Household grocery costs from improved diets</td><td>$385 per person per year transitioned to a better diet</td><td>The annual cost of food in the improved diet described by Springmann et al., (2016) is less than the existing average diet, with food prices in LAC based on Springmann et al. (2017) and adjusted to 2019 dollars.</td></tr><tr><td>Health benefits of better diets</td><td>$1,750/per person per year transitioned to a better diet</td><td>The health benefits of a dietary change is calculated based on the aggregate annual health benefits defined in Springmann et al. (2016) for LAC in 2050. The costs are deeply uncertain: a direct-costs approach estimates costs of $100B in LAC; a value-of-statistical-life approach estimates costs of approximately $2.5 trillion (see Figures S.12 and S.13) divided by the population in LAC projected by the World Bank for 2050 (approximately 750 million). We use an average, but explore the full range, from $350/person to $3,500/person.</td></tr></tbody></table>
<table><caption>Table A.10 Crosscutting agricultural transformations' non-technical benefits and costs</caption><thead><tr><td>Value</td><td>Value (LAC average in 2019 USD) (Positive indicates benefits)</td><td>Notes and data sources</td></tr></thead><tbody><tr><td>Household grocery costs from improved diets</td><td>$385 per person per year transitioned to a better diet</td><td>The annual cost of food in the improved diet described by Springmann et al., (2016) is less than the existing average diet, with food prices in LAC based on Springmann et al. (2017) and adjusted to 2019 dollars.</td></tr><tr><td>Health benefits of better diets</td><td>$1,750/per person per year transitioned to a better diet</td><td>The health benefits of a dietary change is calculated based on the aggregate annual health benefits defined in Springmann et al. (2016) for LAC in 2050. The costs are deeply uncertain: a direct-costs approach estimates costs of $100B in LAC; a value-of-statistical-life approach estimates costs of approximately $2.5 trillion (see Figures S.12 and S.13) divided by the population in LAC projected by the World Bank for 2050 (approximately 750 million). We use an average, but explore the full range, from $350/person to $3,500/person.</td></tr></tbody></table>
# Appendix B. Forests and Land Use

Forest emissions include CO2 sequestration in biomass in primary and secondary forests, as well as harvested wood products, CH4 from mangrove ecosystems, and CO2 from forest fires. Land-use emissions include CO2 emissions derived from converting forest land to other types of land. Land-use changes are specified using a transition matrix for all land-use types and modeled in response to changing demands for livestock and crops. Forestry is divided into primary forest, secondary forests, and mangroves. These categories reflect an aggregation of forestry types into emission-relevant categories. Land-use types include croplands, grasslands, settlements, wetlands, forests-mangroves, forests-primary, and forests-secondary.

## Land-Use Historical Data and Projections

Forest and land-use emissions are a product of the data in Table B.1. Model parameters are calibrated to match model's emissions estimates in the Land Use and Forestry sector available at Climate Watch (2022), combined with forestland sequestration estimates available at the UN FAO Emissions database (FAO, 2023g). The set of calibrated parameters is comprised of transition probabilities for different land-use transitions. Estimated baseline values are modulated to reflect specific national contexts.
# Appendix B. Forests and Land Use

Forest emissions include CO2 sequestration in biomass in primary and secondary forests, as well as harvested wood products, CH4 from mangrove ecosystems, and CO2 from forest fires. Land-use emissions include CO2 emissions derived from converting forest land to other types of land. Land-use changes are specified using a transition matrix for all land-use types and modeled in response to changing demands for livestock and crops. Forestry is divided into primary forest, secondary forests, and mangroves. These categories reflect an aggregation of forestry types into emission-relevant categories. Land-use types include croplands, grasslands, settlements, wetlands, forests-mangroves, forests-primary, and forests-secondary.

## Land-Use Historical Data and Projections

Forest and land-use emissions are a product of the data in Table B.1. Model parameters are calibrated to match model's emissions estimates in the Land Use and Forestry sector available at Climate Watch (2022), combined with forestland sequestration estimates available at the UN FAO Emissions database (FAO, 2023g). The set of calibrated parameters is comprised of transition probabilities for different land-use transitions. Estimated baseline values are modulated to reflect specific national contexts.
<table><caption>Table B.1 Data and methods</caption><thead><tr><td>Data</td><td>Method for historical data</td><td>Method for projections</td></tr></thead><tbody><tr><td>Land-use conversion emission factor</td><td>Emissions factors are derived from biomass stock factors found in IPCC Guidelines for National Greenhouse Gas Inventories, volume 4, Tables 4.12 and 6.4 (IPPC, 2019).</td><td>Emission factors are assumed to be constant.</td></tr><tr><td>Initial land-use area proportion</td><td>Proportions of different land-use types are estimated using the UN FAO Land Use database (FAO, 2023e) and Land Cover database (FAO, 2023f).</td><td>Changes in land-use area are determined endogenously in the model as a function of expected transition probabilities, agriculture production, meat demand and deforestation rates.</td></tr><tr><td>Land-use climate fractions</td><td>Land-use types by Köppen Climate Classification are derived from KGlim 1 km data 1987-2013 averages (Cui et al., 2021). These fractions are combined with climate-dependent default IPCC factors across AFOLU to determine country-level average factors, including emissions from soil mineral carbon, forest sequestration, and biomass emissions from land-use conversion.</td><td>Country climate classification fractions are assumed to be constant.</td></tr><tr><td>Soil organic carbon stocks</td><td>Soil organic carbon stock estimates are based on SoilGrids 1 km 0-30 cm global gridded organic carbon stock data (Poggio et al., 2021).</td><td>Average per unit carbon stocks are assumed to be constant.</td></tr><tr><td>Land-use transition probability</td><td>Estimated using FAO's Land Use database (FAO, 2023e), Land Cover database (FAO, 2023f), and Emissions database (FAO, 2023g). FAO items are mapped into SiSePuede items; transition probabilities are estimated based on observed land-use changes and forest regeneration rates.</td><td>Baseline projection assumes expansion of crops and grasslands and a reduction in primary and secondary forest.</td></tr><tr><td>Forest fire emission factor</td><td>Factors are based on IPCC Guidelines for National Greenhouse Gas Inventories, volume 4, chapter 2, Table 2.4 (IPPC, 2019).</td><td>Factors are assumed to be constant.</td></tr></tbody></table>