# A sectoral model for territorial emission inventory in a low income country - a case study from Madagascar

Rajat Sharma, Erwann Rayssac, Andry Razakamanantsoa, Agnès Jullien

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## To cite this version:

Rajat Sharma, Erwann Rayssac, Andry Razakamanantsoa, Agnès Jullien. A sectoral model for territorial emission inventory in a low income country - a case study from Madagascar. Atmospheric environment: X, 2025, 28, 10.1016/j.aeaoa.2025.100399. hal-05380642

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Submitted on 24 Nov 2025

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# A sectoral model for territorial emission inventory in a low income country - a case study from Madagascar

Rajat Sharma* ID, Erwann Rayssac, Andry Razakamanantsoa, Agnès Jullien

Université Gustave Eiffel, Département GERS-GIE, Bouguenais, 44344, France

## ARTICLE INFO

### Keywords:

- Emission inventory

- Economic weightage

- Air pollutants

- Biomass burning

- Spatial distribution

- Sectoral emission

## ABSTRACT

A territorial Emission Inventory combined with Economic Weightage to quantify regional pollutant emissions in low-income countries is presented. The study covers the 5 regions of Fianarantsoa city in Madagascar and is analyzing the dominant sectors of activities and pollutant concentrations for BC, PM₁₀, PM₂.₅, CO₂, CO, and NOₓ. Ground based surveys are used to assess regional emission related activities. The findings corresponding to residential, hotels and restaurant, open waste burning and transport sectors reveal high levels of total pollutant emissions that is 88.98 T/y of PM, 3.5 T/y of BC, 1375.6 T/y of CO, 158.2 T/y of NOₓ and 7614 T/y of CO₂. The emission estimation combined with economic weightage reveals that despite the residential sector being the primary emitter, hotels and restaurants disproportionately influence emissions and hotspot zones, highlighting the significance of economic activity on emissions. The study highlights significant gaps between regional results from our study when compared to global datasets available in literature, revealing NOₓ emissions of 158.2 T/y, 6–63 times higher than EDGAR (2.5–25 T/y), alongside BC, PM, CO (2–36 times high), and CO₂ (2–8 times high). This study identifies sectoral hotspots and regional emission drivers often overlooked by global top-down inventories. This study highlights a territorial approach that enhances sectoral emission quantification, considering fuel types, sectoral contributions, and economic weightage.

## 1. Introduction

### 1.1. *Needs to assess air quality*

The air quality deterioration cause 7 million deaths, causing 3.1 million premature deaths and 3.2 % of the global burden of disease worldwide every year. This major global concern, aligns with the UN's sustainable development goals emphasizing the need to address overlooked yet significant risks as air pollution. (United Nations, 2015; Cohen et al., 2017). Low income countries bear a greater burden of the global morbidity and mortality from diseases related to poor air quality (Meghji et al., 2021; Yang et al., 2021). Prolonged exposure to ambient air pollution has been consistently associated with adverse health outcomes and development of chronic obstructive pulmonary disease (Park et al., 2021; Bourbeau et al., 2022; Stolz et al., 2022). Over the past few decades, the global urbanization process has not been uniform and has been concentrated in low and middle income countries, accounting for 90 % of the world's urban growth (Mannucci and Franchini, 2017; Zhang et al., 2022). This rapid urban expansion cause transformations

and loads across various sectors, including residential, hotels and restaurants, transportation, and others, leading to increased demand and consumption. These shifts have influenced economic growth while placing significant pressure on environmental management systems (Sharma and Kumar, 2023). As a result, high emissions and energy consumption across these sectors have worsened ambient air quality and strained environmental resources (Wang et al., 2020; Simayi et al., 2020). Hence, the pace of economic development in the low income countries, particularly across African continent resulted in poor air quality. A one-unit rise in urban population increases child mortality by 2 %, while a one-unit rise in employed population raises child and maternal mortality by 10 % and 36 %, respectively (Owili et al., 2017). In contrast, high income countries like France and the EU use advanced inventories like European monitoring and evaluation programme (EMEP)/European Environment Agency (EEA) and high resolution modeling to manage emissions from various sectors including residential, transport and industry, guiding policies like the EU's Zero Pollution Action Plan (EEA, 2025).

<footnote>* Corresponding author.
E-mail address: rajat.sharma@univ-eiffel.fr (R. Sharma).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Regional built-up study area in Fianarantsoa region, Madagascar.</figcaption></figure>

overall emissions. This division helps create a comparative analysis of how similar sectors within each region influence emission, providing valuable insights into their individual and combined contributions. Population data was collected from INSTAT to establish the distribution on the built-up area. The study area was systematically divided into a uniform grid framework to enable spatially resolved EI development. Using QGIS software (3.4), the area was divided into 100 m × 100 m grid cells. This resolution was chosen to capture spatial variations in emission sources. The grid system was used to create a static, high-resolution baseline of pollutant emissions. Through ground surveys, emissions were mapped to each grid cell based on observed activities within the boundaries of each region, ensuring accurate geospatial allocation.

## 2.2. Selection of emission sources in the study area

The sources of air pollution vary, spanning from local to regional influences, depending on their economic significance. Biomass burning serves as the primary source of fuel in the region, contributing significantly to pollution. Major sectors include Residential, Hotels and Restaurants (H&R), Transport, and Open Waste Burning (OWB) (Fig. 1).

The residential sector in the region relies primarily on firewood and charcoal (predominantly Eucalyptus and Pine and wanza (Acacia) wood, along with some use of electrical appliances and liquid petroleum gas. The distribution and classification of H&R in Fianarantsoa are complex. Bigger hotels are limited, while most small restaurants and grill spots are often located within residential buildings, and many double as grocery shops, making it difficult to obtain an official count. The primary sources of waste generation in Fianarantsoa include local residents, commercial establishments, hospitals, hotels, restaurants, and markets. Unfortunately, there is no available secondary data on total waste collection, as the current practice involves burning waste directly in bins on a daily basis. There are 40 cement dustbins distributed across the five regions, each with a standard size of 5m × 3m, provided by the municipality.

The Transport sector includes vehicular emissions from trucks, minibuses, 4-wheelers (4W), 3-wheelers (3W) and 2-wheelers (2W). Madagascar is a low income third world country with no existing policies regarding transport pollution emissions and traffic control strategies. There is no study or report regarding vehicular data or

establishment to monitor the age of vehicles. Vehicle emissions strongly depend on the vehicle fleet composition and traffic volume/conditions (Kuppili et al., 2021; Sarica et al., 2024). The vehicles on Fianarantsoa roads span over 40 years in age, creating a diverse mix of old and new vehicles. Using emission factors under Euro 3 as a baseline for the Madagascar Fianarantsoa region is a reasonable choice given the contextual constraints. Based on survey, most vehicles in the region align between (Euro 1–3) standards due to their age and the dominance of older fleets. Although few newer vehicles are present, the substandard fuel quality in Madagascar often prevents them from achieving their intended emission performance, effectively placing them within the Euro 3 emission range. Madagascar as a nation stands outside Euro 0 in European fuel quality designation with diesel fuel Sulphur content standing at 5000 ppm (UNEP, 2020; Ayetor et al., 2021). As a result of the category of sources and associated fuel consumptions, the selection of BC, PM₁₀, PM₂.₅, CO, CO₂ and NOₓ as main pollutant of interest for EI development is based on their relevance to the dominant fuel use, dependable emission factors and significance on the region. Overall, charcoal and firewood are the primary fuels in each region, with gas and electricity representing only minor contributions to household energy needs. Electricity was excluded from the emission inventory due to its limited availability and negligible contribution to local air pollutant emissions. As reflected in Table 1, electricity accounted for only 4–9 % of total energy use across different sectors, compared to over 50 % for charcoal and up to 38 % for firewood. Moreover, field observations revealed that electricity supply in several parts of the region was irregular, with frequent outages and limited access to electrical appliances. The majority of households and commercial establishments primarily relied on biomass fuels for cooking and heating. In contrast, electricity use was largely restricted to lighting and small devices, activities that have minimal direct pollutant emissions. Furthermore, available electricity consumption data were often incomplete or based on assumptions rather than direct measurements.

In addition, electricity generation in Madagascar is mostly centralized outside the Fianarantsoa region, with the nearest major power plant located approximately 110 km east of Fianarantsoa (World Bank, 2023). According to JIRAMA (Jiro sy Rano Malagasy), the national utility for electricity, over 80 % of installed generation capacity is concentrated in hydroelectric and thermal plants around Antananarivo and in northern regions (IMF, 2025; JIRAMA, 2024). Fianarantsoa's grid is largely
<table><caption>**Table 1**
Fuel use across residential and hotels and restaurants (H&R) sectors.</caption><thead><tr><th rowspan="2">Sector</th><th rowspan="2">Region</th><th colspan="3">Fuel Usage (kg/month)</th><th colspan="4">Fuel Distribution (%) by Use Case</th></tr><tr><th>Charcoal</th><th>Firewood</th><th>Gas</th><th>Charcoal</th><th>Firewood</th><th>Gas</th><th>Electricity</th></tr></thead><tbody><tr><td rowspan="5">Residential</td><td>1</td><td>60.0</td><td>79.7</td><td>10.1</td><td>58.2</td><td>-</td><td>5.2</td><td>-</td></tr><tr><td>2</td><td>169.5</td><td>83.5</td><td>9.4</td><td>69.5</td><td>-</td><td>1.2</td><td>9.3</td></tr><tr><td>3</td><td>131.4</td><td>79.7</td><td>9.8</td><td>69.5</td><td>23.8</td><td>-</td><td>4.6</td></tr><tr><td>4</td><td>132.3</td><td>85.7</td><td>10.1</td><td>52.9</td><td>37.8</td><td>-</td><td>-</td></tr><tr><td>5</td><td>57.7</td><td>80.1</td><td>9.1</td><td>57.7</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="5">Hotels and Restaurants</td><td>1</td><td>102.7</td><td>75.0</td><td>8.5</td><td>36.4</td><td>27.7</td><td>20.0</td><td>15.6</td></tr><tr><td>2</td><td>128.5</td><td>75.0</td><td>8.5</td><td>71.4</td><td>28.5</td><td>-</td><td>-</td></tr><tr><td>3</td><td>443.7</td><td>100.0</td><td>8.5</td><td>66.6</td><td>12.9</td><td>12.9</td><td>7.4</td></tr><tr><td>4</td><td>518.6</td><td>97.0</td><td>8.5</td><td>54.4</td><td>33.6</td><td>4.87</td><td>7.0</td></tr><tr><td>5</td><td>115.0</td><td>80.0</td><td>8.7</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

supplied through transmission from these external production sites, with only small local diesel generators used occasionally for backup during power outages. Therefore, local electricity generation within Fianarantsoa was considered to contribute insignificantly to on-site pollutant emissions, further justifying its exclusion from the local emission inventory. Therefore, to ensure the accuracy and robustness of the inventory, only fuels with well documented consumption patterns were considered.

### 3. Adopted emission factors and activity based model

#### 3.1. Emission factors data

Emission factors (EFs) are a representative number that link the amount of pollutant released to the specific activity connected with that emission in a given context. The chemical properties of a fuel dictate its EFs, which is influenced by combustion, temperature, and control system efficiency. There are no known EFs for Madagascar to our knowledge. Literature provides few measured EFs for African western regions. Incorporating a country and fuel specific suitable EF is a key aspect of the EI development process. EF defines the source importance as emission per unit time and unit process activity. The present study hence used the emission factors, representing African conditions based on several peer reviewed published literature, reports, and policy documents demonstrating emission values within a similar range (EU emission standards, 1992–2005; Marais and Wiedinmyer, 2016; Ferm and Sjöberg, 2015; Liousse et al., 2014; Vicente et al., 2018; Pfotenhauer et al., 2019; Pokhrel et al., 2021; Singh et al., 2023; Mencarelli et al., 2023; Wang et al., 2023). The EFs used in the present work for major sources are outlined in Table 2, their application to Madagascar is considered appropriate due to comparable biomass characteristics and socio-economic contexts. The major residential fuels in Madagascar are charcoal and wood (Table 1), derived from Eucalyptus, Pine, and Wanza (Acacia) species, which exhibit similar moisture content, and carbon

composition to those used in other sub-Saharan regions. Likewise, open cooking style, charcoal kilns, and informal systems resemble those reported in Western and Central African studies and in some Asian contexts involving LPG use resulting in comparable combustion efficiencies. While some variability may exist due to local differences the selected EFs are expected to reasonably represent the emission characteristics of Madagascar’s dominant fuel use patterns.

#### 3.2. Activity based EI emission model

The sources of emission vary locally and regionally. However, what remains the similar in a certain region are the sectors and fuel type contributing. This study utilized an activity based emission factor modeling framework to quantify emissions as base model. The activity data from the survey results are used to create gridded regions. In this approach, total emissions are calculated by multiplying activity data by corresponding emission factors for each grid. The general equation for estimating total emissions is:

$$T_{E_{ij}} = \sum_{i} \sum_{j} (A_{i} * E_{F_{ij}}) \quad (1)$$

where, $T_{E_{ij}}$ represents the total emissions (T/y) from the ith source for the jth pollutant, $A_{i}$ is the activity data of the jth source, and $E_{F_{ij}}$ is the kth pollutant’s emission factor (emissions per unit activity) for the jth source. Emissions from various sources Residential sector, H&R, and OWB were computed separately using source specific emissions equations.

### 4. Methodology to model emissions

Ground surveys were conducted to collect activity data from the study area (Section 4.1). The formulas and methods developed for calculating emissions specific to each sector are presented (Section 4.2). An approach combining emission estimates with economic weightage is explored (Section 4.3).

<table><caption>**Table 2**
Emission factors used for emission estimation.</caption><thead><tr><th>Transport</th><th></th><th>2W (Petrol)</th><th>3W (Diesel)</th><th>Buses (Diesel)</th><th>4W (Petrol)</th><th>4W (Diesel)</th><th>Trucks (Diesel)</th></tr></thead><tbody><tr><td rowspan="5">Euro 1 to Euro 3</td><td>CO</td><td>5.5</td><td>2.72</td><td>0.8</td><td>2.3</td><td>0.64</td><td>0.95</td></tr><tr><td>NOx</td><td>0.250</td><td>0.4</td><td>0.65</td><td>0.15</td><td>0.5</td><td>0.78</td></tr><tr><td>PM</td><td>NA</td><td>0.14</td><td>0.07</td><td>0.15</td><td>0.05</td><td>0.2</td></tr><tr><td>PM<sub>2.5</sub></td><td>0.027</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>PM<sub>10</sub></td><td>0.088</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table><p><strong>Source: EU emission standards (1992–2005) [Note: Emission Factors (g/km)]</strong></p><table><thead><tr><th>Sector</th><th>Fuel</th><th>BC</th><th>PM<sub>10</sub></th><th>PM<sub>2.5</sub></th><th>CO<sub>2</sub></th><th>CO</th><th>NOx</th></tr></thead><tbody><tr><td rowspan="3">Residential and Hotels and Restaurants</td><td>Charcoal</td><td>1*</td><td>10.54<sup>#</sup></td><td>7.38<sup>#</sup></td><td>2352<sup>#1</sup></td><td>189<sup>*</sup></td><td>0.07<sup>-§</sup></td></tr><tr><td>Firewood</td><td>0.83<sup>*</sup></td><td>17.3<sup>§</sup></td><td>11.76<sup>§</sup></td><td>1484<sup>#1</sup></td><td>77<sup>*</sup></td><td>0.03<sup>-§</sup></td></tr><tr><td>Gas (LPG)</td><td>NA</td><td>2.1<sup>§&amp;</sup></td><td>0.33<sup>§&amp;</sup></td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Open Waste Burning</td><td>Waste</td><td>NA</td><td>8<sup>&#</sup></td><td>5.44<sup>&#</sup></td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table><p><strong>Source: * Marais and Wiedinmyer (2016) (DICE Africa), # Mencarelli et al. (2023); Pfotenhauer et al. (2019), &amp; Reddy and Venkataraman (2002), § Pokhrel et al. (2021) (eucaplyptus, whitepine and wanza (Acacia) woods collective average), &darr; Wang et al. (2023), (mixed household/municipal waste), &darr; Liousse et al. (2014) [Note: All the units are in g/kg]</strong></p>
## 4.1. Ground survey to collect local consumption from activity data

This study follows a dual approach of bottom up and top down data use, however it is essential to carry out preliminary ground verification of the activity data to confirm the authenticity of available secondary data to date through representative primary surveys. However, in this study major focus and dependence was on the primary data as secondary data availability is a real issue as management is very limited for the country and region. A full ground survey was performed in the Fianarantsoa city to collect the primary activity data through a campaign involving 40 teams of postgraduate students from Université de Fianarantsoa, who participated following comprehensive orientation training and detailed instructions on the methodology, quality assurance, and control protocols. Survey was organized through questionnaires specific for different sectors to get the information about activity data. The questions were designed in a way to collect information regarding fuel use, activity details, use pattern, vehicular data and family dynamics. To ensure data quality, we conducted two surveys. The first, a trial survey, yielded 560 data points. By analyzing these results, we discovered issues in data collection such as incomplete responses, participant hesitancy, and resistance. We addressed these concerns by refining our approach, retaining the essential and answerable questions, and then deployed a second survey strategically throughout the regions. This revised survey produced 674 data points, offering improved insights and more reliable grounded data which operates at a 2 % margin of error within a 95 % confidence interval, further ensuring the robustness of our findings. The area for survey was selected based on key factors such as population density, housing, markets, and major locations, which are pre studied based on the secondary data collected from Institut National de la Statistique (Instat) and meetings with the administrative officials (municipality, hospitals) to get insights about the activities and region. Teams conducts random surveys, ensuring that only one family per household is selected to avoid data duplication. In Madagascar, it's common for 7–8 families to share a single house, so the total number of residents is recorded for accurate representation. The survey data points are marked on Fig. 1.

## 4.2. Assessment model for local emission sources in Fianarantsoa

Building a reliable emissions inventory relies heavily on both primary and secondary activity data. However, secondary data in Madagascar and much of the African subcontinent remains limited due to policy gaps and other challenges. Notably, our preparation and primary data collection efforts represent the first comprehensive emissions dataset in Madagascar. The primary data collected during our field campaigns has been used to represent emission attached to the local activity's typology.

### 4.2.1. Quantification of activity data and evaluation of emissions from residential sector

The primary survey gathered data on the types and quantities of fuel used by households; if the specific type wasn't known, households reported their monthly expenditure on each fuel type. Across regions 1 to 5, household fuel use patterns reflect a strong dependence on charcoal and firewood, with only minimal use of gas in each region.

Table 1, explains fuel use across Residential and H&R Sectors. Charcoal consumption exhibits significant regional variation, with region 2 leading at 169.54 kg/month and region 1 consuming the least at 60.08 kg/month. Region 3 has the highest percentage of households using charcoal (69.55 %). In contrast, region 4 with 52.9 % of households relying on charcoal and the highest percentage using firewood (37.8 %). Gas and electricity use remain consistently low, with region 1 showing the highest reliance on gas (5.21 %) and region 2 leading in electricity use (9.36 %).

Overall, charcoal and firewood are the primary fuels in each region, with gas and electricity representing only minor contributions to

household energy needs. Emission estimates were quantified using eq. (3)

$$E_{Residential} = \sum_{i} \left[ \left( P_{i} \times F_{uf_{i}} \times T_{a} \times EF_{f_{i}} \right) / 10^{6} \right] \quad (3)$$

where, $E_{Residential}$ are emissions from Residential sector (T/y), $P_i$ is the population in the ith region, $F_u$ is the specific fuel use per household in ith region (kg/month), $EF_{f_i}$ is the emission factor (g/kg) for f fuel and jth pollutant, $T_a$ is the sector specific time duration.

### 4.2.2. Quantification of activity data and evaluation of emissions from hotels and restaurants (H&R)

A survey-based methodology was adopted for H&R situation due to the lack of data and complex situation on ground as explained in section 2.1. The findings indicate that region 1 contains 13.53 % of these establishments, region 2 has 4.09 %, region 3 accounts for 17.18 %, region 4 leads with 28.3 %, and region 5 has 8.8 %. This distribution was used to create a random allocation model in gridded data.

From Table 1, The H&R sector showcases similar biomass dependence, with region 4 consuming the highest average monthly charcoal (518.62 kg) and firewood (97 kg), reflecting its heavy reliance on the fuels. Region 3 follows with significant charcoal use (443.75 kg/month) but shows a comparatively lower dependence on firewood (100 kg/month). Electricity and gas use are minimal across regions, with region 1 showing the highest adoption of electric appliances (15.6 %) and gas use (20.09 %). Region 2's reliance on charcoal is (71.4 %).

Emission estimates were quantified using eq. (4)

$$E_{H&R} = \sum_{i} \left[ \left( \left( C_{n} \times F_{m} \times \left( \frac{T_{h}}{T_{d}} \right) \times EF_{f_{i}} \right) / Ga \right) / 10^{6} \right] \quad (4)$$

where, $E_{H&R}$ are emissions from H&R sector (T/y), $C_n$ is the count of H&R based on survey, $F_m$ is the monthly fuel use (Kg), $EF_{f_i}$ is the emission factor (g/kg) for f fuel and jth pollutant, $T_h$ is the Standard operational time, $Ga$ is area of the specific grid, $T_d$ is daytime hours.

### 4.2.3. Quantification of emissions from open waste burning (OWB) based on potential waste generation

In Africa, the average per capita waste generation is approximately 0.65 kg (Getahun et al., 2012; Sow, 2019). However, due to the absence of recycling facilities and proper treatment options, nearly all waste in the region is improperly disposed of and untreated, primarily being collected in the bins and incinerated. There is no mechanism in place for waste separation. For the purpose of this analysis, we consider burning days as those without rainfall, which totals 192 days in the 2023–24 period (Reliable Prognosis, 2023–24). This open burning of waste is a slow and ongoing process in the region. More than 40 % of the bins are allocated in the region 4 because of the more urban centric built-up environment.

$$E_{OWB} = \sum_{i=1}^{n} \left[ \left( W_{g} \times EF_{i} \times P_{a} \times N_{p} \right) / 10^{3} \right] \quad (5)$$

where, $E_{OWB}$ is the total emission from open waste burning, $W_g$ average per capita waste generation (kg/day), $EF_i$ is the relevant emission factor (g/kg) for open burning, $P_a$ is the population living in the certain grid area around dustbins, $N_p$ represents the number non precipitation days ideal for continuous burning.

### 4.2.4. Quantification of vehicular activity data and evaluation of emissions from transport sector

The regions were categorized into paved roads, unpaved roads, and highways. Survey teams were stationed at traffic intersections to collect comprehensive data. Video recordings were conducted during morning,
afternoon, and evening periods, while survey based questionnaires were administered to gather detailed information on vehicle types and distances traveled. The vehicular emissions for transport sector were calculated using eq. (6)

$$E_v = \sum_k \left( \sum_{j_i} (Ta_k \times Ms_{j_i} \times Ef_{j_i}) \right) / 10^6 \quad (6)$$

where, $E_v$ is the total vehicular emissions (T/y), $Ta_k$ is the transportation activity (VKT in km/day), $Ms_{j_i}$ is the specific proportion of vehicular mode, $Ef_{j_i}$ is the emission factor (g/km), $k$ is the vehicle model type, $j$ represents the vehicles technology (2/4 stroke engine), $i$ represent the fuel type (Petrol/diesel).

### 4.3. Assessment of sectoral emission estimates combined with economic weightage

The sectoral significance in total emissions based on the economic weightage of a particular sector in the region was calculated using eq. (2).

$$R_{SE} = \frac{E_{sec}*}{T_{Eij}} 100 * R_{EW} \quad (2)$$

where, $R_{SE}$ is the rate of sectoral emissions which is percentage emissions generated by the specific sector out of the total emissions; $T_{Eij}$ is total emission estimated for the region; $E_{sec}$ denotes the annual emissions from each sector (T/y) and $R_{EW}$ is percentage economic weightage of the sector for the region as shown in (Table 3).

The sectoral distribution of emissions helps identify hotspot areas, particularly in the study area where air quality management policies are largely non-existent. Population growth and migration are directly linked to increments in different sectoral processes. The economic

weightage helps compare sectors and assess their significance in the overall emissions of different regions. For the estimation of economic weightage, the major sectors in Madagascar that are directly linked to emissions and contribute significantly to the Gross Domestic Product (GDP) were identified (Ministry of Finance, 2023; PSEAU, 2013). The methodology for Fianarantsoa 1 was based on the regional contribution of the Haute Matsiatra region, which accounts for 39.6 % of Madagascar's GDP (Ministry of Finance, 2023-24). The sectors were grouped into four main categories: residential, hotels and restaurants (H&R), open waste burning (OWB), and other industries. In Table 3, the economic weightage (EW) was derived following a transparent and reproducible procedure: column (a) represents the percentage GDP share of each sector in Madagascar; column (b) gives the Fianarantsoa contribution to the national GDP (0.396 = 39.6 %); column (c) is calculated as (0.396 × a), representing the regional GDP contribution of each sector; and the relative economic weightage (EW) is then obtained as EW = (b × c). Finally, the rate of sectoral emissions ($R_{SE}$) was computed using Eq. (2), integrating both the emission magnitude and the sectoral economic significance. All GDP data and sectoral shares correspond to the 2023-24 fiscal year, obtained from the Ministry of Finance (2023-24) and INSTAT (2023), while sectoral classifications were adapted from PSEAU (2013). This method provides a structured approach to quantifying sectoral contributions to regional pollutant emissions by integrating the economic weightage of each sector, offering critical insights into the spatial and sectoral distribution of pollutants. By leveraging sector specific emissions data and incorporating economic weightages, the analysis enables a comprehensive understanding of how various sectors contribute to the total emissions of key pollutants such as PM$_{2.5}$, PM$_{10}$, CO, BC, NO$_{x}$, and CO$_{2}$. The integration of economic weightage ensures that the findings reflect the regional economic structure, highlighting the proportional environmental burden associated with sectoral activities.

<caption>**Table 3**</caption>

Economic weightage determination based on gross domestic product and rate of sectoral contribution from Fianarantsoa (Fig. 1), Madagascar (Ministry of Finance, 2023; PSEAU, 2013). Those quoted<sup>a</sup> are also identified in the survey for local grid data collection.

<table><thead><tr><th rowspan="2">Category of sectors</th><th rowspan="2">Sources by Sector</th><th>Fianarantsoa Contribution to Madagascar's GDP (Gross domestic product)</th><th>Fianarantsoa sectorial Contribution, representing how each source contribute within Fianarantsoa</th><th>Regional GDP Contribution</th><th>Economic Weightage (EW)</th></tr><tr><th>(a)</th><th>(b)</th><th>(c) = (0.369 × a)</th><th>REW = (b+c)</th></tr></thead><tbody><tr><td rowspan="3">Residential Sector<sup>a</sup></td><td>Services to households</td><td>~1 %</td><td>85 %</td><td>1.47</td><td>1.25 %</td></tr><tr><td>Utilities</td><td>~2 %</td><td></td><td></td><td></td></tr><tr><td>Equipment</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td rowspan="2">Hotels and Restaurants (H&R)<sup>a</sup></td><td>Hotels and restaurants</td><td>~4 %</td><td>77 %</td><td>1.84</td><td>1.42 %</td></tr><tr><td>Food and drinks</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td rowspan="3">Agriculture</td><td>Agriculture</td><td>~18 %</td><td>0.45 %</td><td>7.38</td><td>3.321 %</td></tr><tr><td>Livestock and fisheries</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td>Forestry</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td rowspan="6">Other Industries/ Construction<sup>a</sup></td><td>Extractive industry</td><td>~12 %</td><td>81.4 %</td><td>9.22</td><td>7.50 %</td></tr><tr><td>Metal industry</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td>Textile</td><td>~6 %</td><td></td><td></td><td></td></tr><tr><td>Construction material</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td>Transport<sup>a</sup></td><td>~4 %</td><td></td><td></td><td></td></tr><tr><td>Other industries</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td rowspan="6">Others (Indirect Emissions)</td><td>Trade and commerce</td><td>~3 %</td><td>NA</td><td></td><td></td></tr><tr><td>Health</td><td>~2 %</td><td></td><td></td><td></td></tr><tr><td>Administration</td><td>~1 %</td><td></td><td></td><td></td></tr><tr><td>Telecommunication</td><td>~3 %</td><td></td><td></td><td></td></tr><tr><td>Banks and insurance</td><td>~2 %</td><td></td><td></td><td></td></tr><tr><td></td><td>~11 %</td><td></td><td></td><td></td></tr></tbody></table>

<footnote><sup>a</sup> 0.369 = Budget allocation for Haute Matsiatra region (Ministry of Finance, 2023).</footnote>
## 5. Results and discussion

This section presents the results and discussion of annual pollutant emissions across major sectors, following the methodology outlined in Section 4. Evaluation of EI of regional sectors corresponding to Residential and H&R Sectors are discussed in Section 5.1, with annual regional sector specific emission trends detailed in Sections 5.1.1 to 5.1.6. The influence of economic weightage on annual pollutant emissions from these Residential and H&R Sectors is explored in Section 5.2. Section 5.3 covers annual pollutant emissions from the Transport Sector, with regional emission trends analyzed in Sections 5.3.1 and 5.3.2.

Emissions from Open Waste Burning are addressed in Section 5.4. A comparison with global model emissions is provided in Section 5.5. Section 5.6 presents prioritized regions with pollutant emissions and targeted interventions.

The major dependence in Fianarantsoa is on three fuels charcoal, firewood and gas. Fig. (2 and 3) provides a detailed breakdown of pollutant emissions from the residential and H&R sector based on fuel type and regional distribution. Specifically, Fig. (2a, 3a) provides the annual percentage distribution of various pollutants by fuel use. Fig. (2b, 3b) highlights the regional percentage variations in emissions, showing each region's deviation relative to the overall regional emission mean.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Comparative Regional Analysis of Residential Emissions by Fuel Type and Pollutants: (a) Percentage Emission Distributions (b) Regional Variations for BC, PM10, PM2.5, CO, CO2 and NOx relative to the Mean.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 2. (continued).</figcaption></figure>

Lastly, Fig. S1(a, b) presents the total annual emissions for each region and fuel type, differentiated by pollutant. Fig. 4 presents the regional gridded spatial distribution of pollutant emission. Combined Emissions of BC, PM<sub>2.5</sub>, PM<sub>10</sub>, CO, CO<sub>2</sub> and NO<sub>x</sub>, exhibit similar trends, while PM displays a distinct pattern.

## 5.1. Evaluation of EI of regional sectors corresponding to residential and H&R sectors

Across all pollutants the Residential sector emerges as the dominant emission source, consistently contributing the largest annual totals across regions and fuel types. Charcoal and firewood combustion within households dominate, with region 4 alone contributing around or above 50 % of total residential emissions for several key pollutants (notably BC, PM<sub>2.5</sub> from firewood, CO, and CO<sub>2</sub>). Regions 3 and 1 follow but at much lower magnitudes, indicating a concentration of household fuel use and related emissions. while the H&R sector represents a smaller share of regional economic activity, it exerts notably strong and localized emission impacts, particularly in regions 3 and 4, where dense clusters of eateries and solid-fuel cooking practices cause emission levels exceeding residential in few cases. This pattern highlights the dominance of the Residential sector as the primary emission driver, but also

the disproportionate local influence of the H&R sector, where fuel use and inefficient combustion collectively result in an outsized emission relative to sectors smaller economic share.

### 5.1.1. Annual sectoral BC emission trends across regions

BC emissions are highest in the Residential sector of region 4, which alone contributes 58 % of total charcoal BC. Region 4's dependence on both charcoal (1.02 T/y) and firewood (0.47 T/y) makes it the hotspot. In the H&R sector, region 4 again leads (0.35 T/y), followed closely by region 2 (0.28 T/y), confirming that even smaller activities can affect residential zones in emissions strength.

#### 5.1.1.1. Residential sector.
Region 4 dominates BC emissions from charcoal in the residential sector, from Fig. 2(a), emitting 1.02 T/y (58 % of the total), outpacing region 3 (0.45 T/y, 28 %) by 127 % and region 1 (0.22 T/y) by 364 %. Compared to region 5 (0.02 T/y) and region 2 (0.007 T/y), region 4's emissions soar by 5000 % and 14,400 %, respectively. For firewood, region 4 again leads with 0.47 T/y (63 %), exceeding region 1 (0.12 T/y) by 292 %, region 3 (0.08 T/y) by 488 %, region 5 (0.06 T/y) by 683 %, and region 2 (0.001 T/y) by an astonishing 46,900 %. Fig. 2(b) underscores this dominance: charcoal emissions in region 4 exceed the mean by +307.4 %, signaling heavy
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Comparative Regional Analysis of H&R Emissions by Fuel Type and Pollutants: (a) Percentage Emission Distributions (b) Regional Variations for BC, PM₁₀, PM₂.₅, CO, CO₂ and NOₓ relative to the Mean.</figcaption></figure>

reliance, while region 3 follows at +95.2 %. Regions 5, 2, and 1 lag below the mean by -11.3 %, -97.1 %, and -97.6 %, respectively. Firewood emissions in region 4 are +87.4 % above the mean, a potential hotspot, while other regions drop sharply: 52.1 % (region 1) to -89.4 % (region 2).

5.1.1.2. *H&R sector.* In the H&R sector, region 4 tops charcoal BC emissions at 0.35 T/y (43 %), from Fig. 3(a and b), followed by region 2 (0.28 T/y, 35 %) and region 3 (0.14 T/y, 17 %). Regions 1 and 5 contribute minimally at 0.015 T/y (2 %) and 0.009 T/y (1 %). Relative to the mean (0.158 T/y), regions 4, 2, and 3 rise by +280.2 %, +210.4 %, and +60.7 %, while regions 1 and 5 plummet by -83.8 % and -89.5 %. For wood, region 2 leads with 0.06 T/y (55 %), followed by region 4 (0.03 T/y, 30 %) and region 3 (0.005 T/y, 9 %). All regions fall below the mean, with reductions from -33 % (region 2) to -94.8 % (region 5).

5.1.2. *Annual sectoral PM<sub>2.5</sub> emission trends across regions*

A distinct fuel-based contrast emerges in region 3 leads PM<sub>2.5</sub> from charcoal (3.63 T/y), while region 4 dominates firewood (5.96 T/y). Together they represent the majority of residential PM<sub>2.5</sub> output. For the H&R sector, region 4 (2.60 T/y) alone contributes nearly half the total, over 40 times more than region 2, revealing that localized commercial cooking is a key PM<sub>2.5</sub> driver despite its smaller scale.

5.1.2.1. Residential sector. Region 3 leads PM₂.₅ emissions from charcoal at 3.63 T/y (44 %), 71.2 % above region 2 (2.12 T/y, 26 %), which exceeds region 1 (1.365 T/y) by 55.3 %. Region 1 outstrips region 4 (0.60 T/y) by 127.5 %, while region 4 surpasses region 5 (0.15 T/y) by 300 %. Firewood emissions peak in region 4 at 5.96 T/y (55 %), 250.6 % above region 1 (1.7 T/y, 16 %), followed by region 3 (1.20 T/y) and region 5 (1.13 T/y). Region 2 trails at 0.88 T/y. Gas emissions, though minor, peak in region 4 at 0.0027 T/y, 107.7 % above region 5 (0.0013 T/y). Fig. 2(b) shows region 4's wood emissions soaring +369 % above
<figure><img src="image_8.png" /><figcaption>Fig. 3. (continued).</figcaption></figure>

the mean, while charcoal emissions in region 3 spike by +185.4 %. Regions 4 and 5 fall below the mean for charcoal by -52.4 % and -87.8 %.

5.1.2.2. **H&R sector.** Region 4 shows the highest charcoal emissions at 2.60 T/y, 677.7 % above the mean of 0.78 T/y, followed by region 3 at 1.10 T/y (228.6 %). Regions 1, 2, and 5 are below the mean, with reductions of 66.8 %, 84.1 %, and 78.8 %, respectively. Region 4 emits over twice as much as region 3 and more than 40 times region 2. For wood emissions, region 4 leads with 0.48 T/y, 45.5 % above the mean of 0.22 T/y. Regions 1, 3, 5, and 2 record reductions of 73.5 %, 76.6 %, 78.5 %, and 93.8 % below the mean, respectively. The significant difference, including the nearly 24 fold gap between region 4 and region 2, highlights variations in wood use.

### 5.1.3. Annual sectoral PM₁₀ emission trends across regions

Residential charcoal PM₁₀ is highest in region 3 (5.18 T/y), while wood PM₁₀ is led by region 4 (1.20 T/y). In H&R, region 4 (3.71 T/y) emits nearly 25 times region 2, confirming its extreme dominance for coarse particulates.

#### 5.1.3.1. Residential sector. In the residential sector, region 3 records the

highest PM₁₀ emissions from charcoal, with 5.18 T/y, followed by region 2 at 3.03 T/y, region 1 at 2.35 T/y, region 4 at 0.84 T/y, and region 5 at 0.22 T/y. Region 3's emissions are 459.3 % above the regional mean, while region 2 and region 1 show increases of 227.4 % and 154.2 %, respectively. In contrast, regions 4 and 5 report reductions of -8.9 % and -76 %, indicating either lower charcoal consumption or more effective emission control measures. The significant disparity highlights region 3's high reliance on charcoal compared to others. For PM₁₀ emissions from wood, region 4 leads with 1.20 T/y, significantly exceeding the regional mean by 29.9 %, followed by region 1 (0.37 T/y), region 3 (0.26 T/y), region 5 (0.23 T/y), and region 2 (0.18 T/y). The other regions exhibit reductions from the mean, with region 1 at -60 %, region 2 at -80 %, region 3 at -71.8 %, and region 5 at -74.54 %.

5.1.3.2. *H&R sector.* In the H&R sector, region 4 again dominates PM₁₀ emissions from charcoal with 3.71 T/y, surpassing the regional mean (1.04 T/y) by 673.4 %. Region 3 follows with 1.57 T/y, 226.8 % above the mean. In contrast, regions 1 (0.15 T/y), 5 (0.1 T/y), and 2 (0.07 T/y) exhibit reductions of -67 %, -78.6 %, and -84.2 %, respectively. The stark disparity is evident as region 4's emissions are nearly 25 times higher than region 2's, underlining the need for targeted interventions in these high-emission areas. For PM₁₀ emissions from wood, region 4
<figure><img src="image_9.png" /><figcaption>Fig. 4. Spatial distribution of gridded pollutant emissions across five regions of Fianarantsoa: Residential and H&R sectors - combined distribution of PM and BC (T/y).</figcaption></figure>

remains the highest contributor at 0.71 T/y, 49.1 % above the regional mean of 0.32 T/y. The remaining regions region 1 (0.13 T/y), region 3 (0.11 T/y), region 5 (0.10 T/y), and region 2 (0.03 T/y) show reductions from the mean, at -72.8 %, -76 %, -78.6 %, and -93.6 %, respectively. Region 4's emissions are more than 23 times higher than region 2's, emphasizing its significant contribution to regional PM₁₀ levels.

### 5.1.4. Annual sectoral CO emission trends across regions

CO emissions are mostly concentrated in region 4, which alone produces ~194 T/y of residential charcoal CO (>50 % of the total). The H&R sector mirrors this pattern: Region 4 (66.7 T/y) emits more than double region 3, confirming that combustion efficiency is lowest in region 4 across both sectors.

#### 5.1.4.1. Residential sector.
In the residential sector, region 4 is the largest emitter of charcoal derived CO, producing 194.13 T/y substantially higher than other regions. This figure likely exceeds 50 % of the total emissions from this source across all regions, reflecting a dominant reliance on charcoal burning in region 4. Region 3 follows with 93.01 T/y, nearly half of region 4's emissions. Region 2 contributes 54.44 T/y, followed by region 1 with 42.26 T/y, and region 5 at 3.98 T/y, the lowest value. For wood derived CO emissions, region 4 again leads with 11.2 T/y, though the margin between it and other regions is smaller. Region 1 emits 7.9 T/y, followed by region 3 at 6.48 T/y, region 2 at 5.7 T/y, and region 5 at 3.98 T/y. This distribution suggests that wood use for CO emissions is more evenly spread across regions compared to charcoal, though region 4 maintains the highest share. Fig. 2(b) highlights CO emissions relative to the mean regional value, revealing distinct patterns. For charcoal emissions, regions 4, 3, and 2 report increases above the mean, with region 4 showing the largest rise at 319.3 %, followed by region 3 at 100.9 % and region 2 at 17.6 %. In contrast, regions 1 and 5 show reductions, with region 1 at -8.7 % and region 5 at -91.4 %. For wood emissions, all regions fall below the mean. Region 4 has the smallest reduction at -5.3 %, while regions 1, 2, 3, and 5 show more significant decreases of -75.8 %, -87.5 %, -82.9 %, and -86 %, respectively.

#### 5.1.4.2. H&R sector.
In the H&R sector, region 4 dominates charcoal CO emissions, contributing 66.69 T/y, significantly surpassing region

3's 28.17 T/y by 136.7 %. Emissions from regions 1, 5, and 2 are considerably lower at 2.84, 1.96, and 1.36 T/y, respectively. Compared to region 1, region 4's emissions are 2248 % high, and the disparity grows further with regions 5 and 2, at 3305 % and 4796 %, respectively. Against the regional mean, regions 3 and 4 show increases of 166 % and 529.5 %, while regions 1, 2, and 5 exhibit reductions of -73.1 %, -87.1 %, and -81.5 %. For wood-derived CO emissions, region 4 leads again with 3.19 T/y, surpassing region 1 by 450 % and regions 3, 5, and 2 by 538 %, 697.5 %, and 3090 %, respectively. Despite these disparities, all regions report reductions from the mean, with values of -94.5 %, -98.7 %, -95.2 %, -69.9 %, and -95.5 % for regions 1 through 5. This highlights the relatively minimal CO emissions from wood across most regions, with region 4 standing out as a significant contributor.

### 5.1.5. Annual sectoral CO₂ emission trends across regions

CO₂ pattern mirror CO, strong charcoal dominance in region 4 across both sectors. Region 4, CO₂ (2415 T/y) is more than twice region 3 (1157 T/y), while H&R region 4 (830 T/y) exceeds region 3 by 137 %.

#### 5.1.5.1. Residential sector.
Region 4 leads charcoal CO₂ emissions at 2415 T/y (+285.6 % above mean), followed by region 3 (1157.4 T/y, +84.8 %) and region 2 (677.4 T/y, +8.1 %). Regions 1 (526.02 T/y) and 5 (49.59 T/y) drop by -16 % and -92.1 %. Wood emissions peak in region 4 at 844.8 T/y (+34.8 %), while others decline: 65.5 % (region 1) to -82.3 % (region 2).

#### 5.1.5.2. H&R sector.
Region 4 tops charcoal CO₂ emissions at 829.93 T/y (+514.9 %), 136.7 % above region 3 (350.65 T/y, +159.8 %). Regions 1, 5, and 2 fall by -73.7 %, -83 %, and -87.4 %. Wood emissions peak in region 4 at 61.49 T/y, but all regions drop below the mean, from -54.4 % (region 4) to -91.7 % (region 1).

### 5.1.6. Annual sectoral NOₓ emission trends across regions

NOₓ emissions peak in region 4, reaching 0.07 T/y for residential charcoal (+316 % above mean) and 0.02 T/y for H&R (+529 %). Regions 1 and 5 remain <10 % of that level. The clear regional clustering of NOₓ with carbon-rich fuels confirms the common combustion source pattern.
5.1.6.1. Residential sector. Region 4 leads charcoal NOₓ emissions at 0.07 T/y (+316.3 % above mean), followed by region 3 (+99.4 %) and region 2 (+16.7 %). Regions 1 and 5 fall by -9.4 % and -91.5 %. Wood emissions peak in region 4 at 0.017 T/y (-1.1 % below mean), with others dropping sharply: 74.7 % (region 1) to -87 % (region 2).

5.1.6.2. H&R sector. Region 4 dominates charcoal NOₓ emissions at 0.02 T/y (+528.9 %), 100 % above region 3 (0.01 T/y, +165.7 %). Regions 1, 5, and 2 plummet by -73.1 %, -82.6 %, and -87.1 %. Wood emissions peak in region 4 at 0.0012 T/y, but all regions fall below the mean, from -68.3 % (region 4) to -94.2 % (region 1).

## 5.2. Significance of economic weightage on annual pollutant emissions from residential and H&R sectors

Economic weighting reveals that although the Residential sector retains the highest significance across all pollutants, the H&R sector's contribution is highly disproportionate in regions 3 and 4 ranging up to 45–97 % for certain pollutants (notably PM₁₀ and PM₂.₅). The economic footprint of restaurants is small, yet their emission intensity is high.

Fig. 5 represents economic weightage for regions 1, 5, and 4, Residential significance weightages are exceptionally high, with values of 117.7 %, 107.8 %, and 99.3 %, respectively. In comparison, the H&R sector's significance is minimal, with values ranging from 8.3 % to 19.5 %. Regions 2 has the highest H&R value for BC, however the H&R establishments in the region are lesser compared to other regions, this is due to the high consumption of wood in region 2. Although residential sectors remain the primary contributors (98.6 % in region 3), H&R significance are slightly more notable, with a 30 % contribution. The economic significance of BC emissions is predominantly from the residential sector in all regions, with regions 3 and 5 showing a modest contribution from small restaurants. But the slightly high dominance of wood use surges the economic significance of restaurants in region 2, making it hotspot intervention zone, especially for H&R.

For PM₂.₅ emissions, residential sectors generally maintain high economic weightage, with notable H&R contributions in some regions. From Fig. 5, regions 1 and 2, residential sector is dominant with weightages of 118 % and 122 %, respectively, while H&R contributions

remain low (8 % in region 1 and 3.4 % in region 2). Regions 3 and 4, Residential contributions are high (100.5 % in region 3 and 112.6 % in region 5), but H&Rs also hold a significant share, with 27.8 % in region 3 and lesser 14.1 % in region 5. Region 4, Residential significance is lower at 85 %, while H&R contribution is 45.4 %. PM₂.₅ emissions economic significance is primarily from residential sources, though regions 3 and 4 show meaningful H&R contributions. Focus for intervention in regions 3 and 4 may need to address both sectors, while regions 1, 2, and 5 can prioritize residential emissions.

The economic significance on PM₁₀ emissions varies more significantly between regions and sectors. Regions 1 and 2, Residential sectors have a very high economic significance (113 % in region 1 and 121 % in region 2), with H&R contributing minimally (13.7 % in region 1 and 4.6 % in region 2). Regions 3 and 5, These regions display a more balanced contribution, with residential weightages of 95.4 % and 86 %, and H&R significance of 33.6 % and 44.3 %, respectively. Region 4 with residential sources contributing 39.5 % and H&R 97.1 %. PM₁₀ emissions making it the hotspot zone. Region 5 show the highest dual sector significance. Regions 3, 4, and 5, all have considerable economic weightages for both sectors. Targeted interventions addressing both sectors would be most beneficial in these regions, while regions 1 and 2 could focus more on residential emissions. The variation between PM₂.₅ and PM₁₀ economic significance is because of the more footfall around the H&R areas which is high in region 4.

The residential sector consistently shows high economic weightage in CO emissions across regions. From Fig. 5, regions 1 and 2, residential significance weightages reach 117.6 % and 122 %, with a negligible contribution from H&R (3.4 % in region 2 and 8.4 % in region 1). This indicates residential activities as the primary economic contributors to CO emissions. Regions 3 and 4, residential emissions remain dominant (~98 % in both regions), H&R contributions are high here, with 30.7 % in region 3 and 30.6 % in region 4, suggesting a more balanced economic significance between the two sectors. Region 5, residential CO is high at 104.6 %, while the H&R sector contributes 23.1 %, indicating the importance of residential sector in this region. CO emissions have a strong economic significance from residential sectors across all regions, but regions 3, 4, and 5 also show significant contributions from the H&R sector.

<figure><img src="image_10.png" /><figcaption>Fig. 5. Sectoral emission estimates combined with Economic Weightage.</figcaption></figure>
For CO₂, regions 1 and 2, residential sectors have a significant weightage of 117.6% and 122%, respectively, with minor contributions from H&R (8.4% in region 1 and 3.4% in region 2). In regions 3, the residential sector remains predominant (98%), H&R hold a considerable share, with 30.6% in region 3 and 30.5% in region 4. Region 5, residential significance on CO₂ is slightly lower at 104.8%, while the H&R sector contributes a significant 22.9%. Similar to CO emissions, CO₂ economic significance is primarily from residential sources. However, regions 3, 4, and 5 show meaningful H&R contributions. For NOₓ, regions 1 and 2, residential weightages are highest at 117.5% and 122%, with minimal contributions from the H&R sector (8.5% in region 1 and 3.5% in region 2). Regions 3 and 4, Residential contributions are slightly lower here (97.4% in region 3 and 96.8% in region 4), with H&R contributing significantly at 31.4% and 32.1%, respectively. Region 5, The residential sector has a significance weightage of 101.9%, while H&R account for 26.2%, indicating both sectors as key contributors. NOₓ emissions exhibit a dual sector economic significance in regions 3, 4, and 5, where both residential and H&R sectors contribute meaningfully. Regions 1 and 2, however, are predominantly influenced by residential activities. Region specific strategies should therefore focus on both sectors in regions 3, 4, and 5, while prioritizing residential emissions in regions 1 and 2.

### 5.2.1. Identification and drivers of regional hotspots

Across all analyses, regions 4 and 3 clearly emerge as the dominant emission hotspots, consistently recording the highest pollutant emissions and sectoral economic weightages. The pattern represents region 4 alone contributing 58% of residential BC, over 50% of CO and CO₂, and the largest shares of PM₂.₅ and PM₁₀ from firewood, while the H&R sector in region 4 emits up to 97% of total PM₁₀ and nearly half of all PM₂.₅. Region 3 follows with high emissions from charcoal based PM₂.₅ and PM₁₀, reflecting higher household fuel use and growing commercial activity.

The high emission values in both sectors correspond to the densely populated, economically active urban areas of these regions, where compact settlements and concentrated restaurants increase fuel demand and combustion frequency. Region 4's dominance in both residential and H&R emissions indicate a spatial overlap of domestic and commercial cooking sources. However, regions 1, 2, and 5 exhibit lower emissions and economic weightages, confirming that the observed hot-spot behavior in regions 4 and 3 is directly from the measured emissions of fuel use, combustion inefficiency, and population cluster reflected in the emission inventory results.

### 5.3. Annual pollutant emissions from transport sector

The following section presents the results based on the pollutant emissions data from the transport sector, as illustrated in Fig. (6 and S2). These figures provide a detailed analysis of emissions categorized by fuel type and regional distribution. Fig. 6(a–d) highlights the pollutant emissions across different vehicle types at various times of the day, while Fig. S2(a–d) focuses on transport emissions and their percentage variations relative to the overall regional mean. Fig. 6(e) presents the spatial pollutant emission distributions for all the vehicle types on the road network.

#### 5.3.1. Sectoral PM petrol and diesel emission trends across regions

Region 1 is the largest emitter of both petrol and diesel based PM, with 4 wheelers dominating emissions. Petrol PM peaks at 4.19 T/y during the evening, while diesel PM reaches 1.40 T/y in the same period, Fig. 6(a and b). Region 4 follows closely, with petrol PM at 3.65 T/y and diesel PM at 1.22 T/y during the evening. Regions 2, 3, and 5 show much lower emissions for both PM types, generally below 0.5 T/y across all vehicle types and times. Evening emissions from region 1's 4 wheelers are +298.3% above the mean for petrol PM and +425.9% for diesel PM. Region 4 also reports significant increases, with petrol PM emissions in the evening at +246.7% and diesel PM in the morning at +396.1%. In contrast, emissions from 3 wheelers and trucks in regions 3 and 5 show reductions of -80% or more for both PM types, emphasizing their minimal contribution. 4 wheelers are the primary contributors to both petrol and diesel PM in regions 1 and 4, accounting for over 80% of emissions. 3W and trucks are minor contributors across all regions. Evening emissions dominate for both petrol and diesel PM, followed by afternoon levels. Morning emissions are consistently the lowest, reflecting reduced vehicle activity during early hours.

From Fig. S2(a) PM (Petrol), region 1 shows significant positive variations, with 4W in the afternoon at 135.7% and 2W in the afternoon at 104.7%, indicating substantial emissions from personal vehicles. Morning emissions for 4W also peak at 107.7%, reflecting busy travel time. Region 2 presents mixed trends, with notable reductions. 4W in the afternoon drop by -73.3%, while 4W in the morning decline by -69.9%, suggesting lower emissions during these periods. Region 3 is dominated by reductions, with 4W in the afternoon decreasing by -82.2% and 4W in the morning by -79.6%, reflecting effective emission control or reduced transport activities. Region 4 displays a balance between high increases and reductions. Morning emissions from 4W peak at 121.6%, but evening emissions for 2W is positive at 29.6%, showing a

<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Comparative Analysis of Regional Transport Emissions based on Vehicle Type, Fuel Type, Pollutant Levels, and Temporal Variations: (a) PM (Petrol) (b) PM (Diesel) (c) CO (d) NOₓ (e) Transport Sector: Transport Sector Spatial Emission Distribution (T/y).</figcaption></figure>
shift in emission patterns. Region 5 exhibits overall negative variations, with consistent declines across all categories, such as 2W in the evening at -87.5 % and 4W in the morning at -85.9 %, suggesting better emission management.

For PM (diesel), Fig. S2(b) shows region 1's high level of variability, with the highest positive peaks reaching up to 244.2 % for buses in the evening and 222.4 % for trucks in the evening. The morning 3 wheelers in the region 1 are showing negative trends of -42.4 % compared to regional mean. In contrast, region 2 and region 3 displays a complete negative variation, with some vehicle types like 3W in the afternoon and buses in evening showing significant reductions of (-73.3 % and -90.5 %). Region 4 sees the highest positive spike for buses in the morning at 161.9 %, while exhibit a substantial drop (-57.5 %) in the afternoon. Region 5 is marked by overall negative variations, suggesting that diesel PM emissions are relatively lower or less variable in these regions during the observed periods.

The higher diesel emissions in regions 1 and 4 are mainly because the national highway passes through these areas. This route brings a constant flow of buses and trucks, especially during the late afternoon and evening. These heavy vehicles mostly use diesel, which explains why diesel PM has higher emissions in region 4 during the evening. The timing also matches the return of intercity traffic and higher road activity. Petrol PM comes mostly from 4W to 2W moving within the city. This is why petrol PM is more spread across regions, while diesel PM is concentrated near the highway. Overall, evening peaks show the combined effect of city travel and highway transport, while mornings remain low due to limited vehicle activity.

### 5.3.2. Sectoral CO and NOₓ emission trends across regions

Region 1 is the largest emitter of both CO and NOₓ, with 2W being the primary contributors. Afternoon emissions dominate, with CO emissions reaching 34.94 T/y and NOₓ at 1.91 T/y. Region 4 follows closely for CO, with 25.88 T/y from 2W in the afternoon, while for NOₓ, buses in region 4 contribute moderately at 0.89 T/y during the afternoon. Regions 2, 3, and 5 report significantly lower emissions for both pollutants, with CO emissions generally under 1.5 T/y and NOₓ emissions under 0.1 T/y across vehicle types and time periods.

CO emissions in region 1 during the afternoon exceed the regional mean by +177.1 %, while region 4 shows a +121.3 % increase. NOₓ emissions from region 1's 2W during the afternoon are +189.2 % above the mean. In contrast, regions 2, 3, and 5 consistently report reductions, with 3W and trucks showing declines of -90 % or more for both pollutants. Region 5's NOₓ emissions from buses during the afternoon drop by -87.8 %, highlighting the significant regional disparities. 2W are the dominant source of CO and NOₓ emissions in regions 1 and 4. Trucks and 3W are minor contributors across all regions. Afternoon emissions are consistently the highest for both pollutants across all regions, driven by peak traffic from 2W. Evening emissions rank second, while mornings exhibit the lowest levels. This trend reflects typical daily traffic patterns, with high vehicle use during work hours.

Fig. S2(c) highlights significant regional and temporal variations in CO and NOₓ transport emissions by vehicle type and time of day. Region 1 exhibits the highest positive variations, with 4 wheelers in the evening reaching 244.2 % and trucks in the evening at 222.6 %, highlighting the dominance of heavy-duty vehicles and 4 wheelers in emissions. In contrast, region 3 shows predominant reductions, with 3W in the afternoon at -82.2 % and trucks in the evening at -90.5 %, indicating lesser volume. Similarly, region 5 demonstrates consistent negative variations, with buses in the evening at -91.3 % and trucks in the morning at -83 %. Region 4 balances sharp positive spikes with reductions, such as trucks in the morning showing a significant increase at 161.9 %, while buses in the evening decrease by -57.5 %. Region 2 displays negative trends, with buses in the evening at -74.7 %, while 4 wheelers in the evening decline by -65.8 %. Vehicle-wise, buses and trucks contribute the most to emission spikes, particularly in mornings and afternoons.

Fig. 6(e) shows spatial variations between emissions from PM (Petrol) and PM (diesel) which is pointing towards a clear bias of diesel based vehicles in the region 1 and 2. This is noticeable in the PM emissions as well as NOₓ and CO emissions. The reason behind such biasness is the travel routes of the buses and trucks and the economic value of the region. Region 1 and 4 have the national highway passing and most of the buses, trucks cars coming from the outside the regions are mostly visiting the region 4 as destination and returning back, hence the bias or categorical heaviness we see in the spatial emissions compared to other regions. PM (Petrol) emissions, however are not bound to this hence a spatial variation can be seen across regions with the specific hotspots.

The observed afternoon and evening peaks in CO and NOₓ emissions are indicative of temporal traffic behavior and emission intensities. Afternoon peaks align with high 2W and private vehicle use within cities, while evening surges reflect heavy duty diesel vehicles entering or exiting through the national highway in regions 1 and 4. These regions act as transport and economic hubs, drawing significant passenger traffic during day-to-day operations. This temporal and spatial overlap explains both the diesel bias and the higher pollutant loads observed compared to interior regions, which remain less affected by external vehicular traffic.

### 5.4. Pollutant emissions from open waste burning

From Fig. S3, region 4 emerges as the dominant contributor, accounting for 90.22 % of the total emissions for both pollutants, while region 2 exhibits the lowest contributions at just 1.31 %. Trend analysis based on regional mean emissions reveals that only region 4 shows a positive trend for both PM₂.₅ and PM₁₀ (both has similar trend), with mean pollutant levels surpassing the overall average of 0.00026 T/y for PM₂.₅ and 0.00038 T/y for PM₁₀. The remaining regions exhibit negative trends, with emissions below this overall average. These disparities are shaped by factors of population density, urbanization, location of dustbins, which in this case is prioritized to region 4 as discussed in (section 2.2).

The OWB estimates are limited to PM emissions due to the lack of detailed knowledge and data on the specific types of organics and inorganics being burned, which would require dedicated research. Characterizing the chemical composition of emissions, such as volatile organics, heavy metals, and inorganics, is crucial for understanding their broader environmental and health impacts. The findings underscore the urgent need for targeted interventions in high emission regions like region 4, including investments in sustainable waste management infrastructure, stringent enforcement of anti burning policies, and community education campaigns.

### 5.5. Comparison of emission with the global models

The absence of region specific emission data for Madagascar in

<table><caption>**Table 4**

Comparison of the total emission by sector of the present study with the global models.</caption><thead><tr><th rowspan="2">Studied area</th><th>This Study</th><th>DACCIWA</th><th>EDGAR (T/ y)</th><th>% Difference vs. DACCIWA</th><th>% Difference vs. EDGAR</th></tr><tr><th>(T/y)</th><th>(T/y)</th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Spatial Resolution</td><td>0.1 km × 0.1 km</td><td>10 km × 10 km</td><td>10 km × 10 km</td><td></td><td></td></tr><tr><td>BC</td><td>3.5</td><td>10-20</td><td>0-2.5</td><td>-76.7 %</td><td>180.0 %</td></tr><tr><td>PM</td><td>88.9</td><td>-</td><td>2.5-25</td><td>N/A</td><td>547.1 %</td></tr><tr><td>NOx</td><td>158.2</td><td>40-70</td><td>2.5-25</td><td>187.6 %</td><td>1050.5 %</td></tr><tr><td>CO</td><td>1375.6</td><td>-</td><td>100-250</td><td>N/A</td><td>686.1 %</td></tr><tr><td>CO₂</td><td>7614</td><td>-</td><td>1000-5000</td><td>N/A</td><td>153.8 %</td></tr></tbody></table>
global inventories underscores the critical contribution of this study (Table 4). The DACCIWA inventory, designed for West African urban areas, reports NOₓ emissions of 40–70 T/y and BC emissions of 10–20 T/y at a 10 km × 10 km resolution, but its regional focus limits its relevance to Madagascar, as evidenced by a –76.7 % difference in BC. Similarly, EDGAR, with the same coarse resolution, estimates emissions for Fianarantsoa at 0–2.5 T/y for BC, 2.5–25 T/y for PM and NOₓ, 100–250 T/y for CO, and 1000–5000 T/y for CO₂. These global models, reliant on aggregated data and default emission factors, often underestimate emissions in data scarce regions, as shown by differences of 180 % for BC, 547 % for PM, 1050 % for NOₓ, 686 % for CO, and 154 % for CO₂ compared to this study’s estimates of 3.5 T/y, 88.98 T/y, 158.2 T/y, 1375.6 T/y, and 7614 T/y, respectively. As quantified in Table 4, the discrepancies are substantial but not merely numerical in nature but the result of limitations of the global emission models. The coarse spatial resolutions and dependence on the national statistics of global inventories like EDGAR and DACCIWA leads to smoothing of the emission peaks causing a dilution of the local high emissions. The heavy reliance on the aggregated data in global models masks the widespread use of local trends and patterns such as dominance of biomass and charcoal combustion, continuous open waste burning and effect of small commercial emission sources. The largest underestimation is in the terms of vehicular fleet, as ground reality shows a fleet of aged vehicles with minimum maintenance which cause unique emission patterns that remain invisible to top-down global datasets. The lack of reliable input data on such informal sectors leads to systematic underrepresentation of actual emissions. Additionally, the use of static temporal profiles in global inventories neglects general daily trends linked to fuel use and transport behaviours that amplify emissions during specific periods. Hence, the magnitude of observed differences reflects not an overestimation in this study but the limitations of generalized global models in capturing the ground realities. This study, therefore, not only fills a data gap but also advances scientific understanding of emission sources in under explored regions, contributing valuable insights to the global knowledge base on emissions in such areas.

## 5.6. Prioritized regions with pollutant emissions and targeted interventions

Intervention studies are provided in (Table 5) from the lower, middle and high income countries i.e. developing and developed nations, resembling the similar sources and are helpful in addressing tailored strategies for localized emissions. These approaches emphasize the promotion cleaner cooking fuels as immediate, feasible solutions to mitigate localized air pollution. Region 4, the highest priority area, exhibits elevated emissions including BC at 1.88 tons per year (T/y), PM₁₀ at 6.48 T/y, PM₂.₅ at 9.66 T/y, CO at 604.39 T/y, CO₂ at 4152.154 T/y, and NOₓ at 58.09 T/y. Proposed actions include providing financial incentives to boost adoption of cleaner fuels, and conducting community education on health impacts and air pollution. Drawing from Ghana's Rural LPG Program (Asante et al., 2018), which successfully increased LPG adoption in rural areas through subsidies and infrastructure development, region 4 can address immediate emission hotspots. The UK's Clean Air Act of 1956, which established smokeless zones to phase out coal burning in households, reduced London's particle pollution drastically within a decade. Similarly, France's nationwide ban on open burning of green waste, in place since 2011, prohibits the burning of biomass residues like grass, leaves, branches, and plant trimmings to avoid releasing toxic fine particles, instead encouraging low cost, accessible alternatives such as composting and mulching that can be implemented at the household or community level. Ghana's accessible incentives, France's low-cost biomass waste management practices, and the UK's regulatory zoning to enforce cleaner fuel shifts, accelerating infrastructure and education for rapid multi pollutant reductions in high emission zones are beneficial for pollutant reduction. Region 3, with BC at 0.73 T/y, PM₁₀ at 7.13 T/y, PM₂.₅ at 6.01 T/y, CO at 179.78 T/y, and

<table><caption>**Table 5**
Region-specific pollutant emissions and proposed interventions.</caption><thead><tr><th>Region (Descending order of Emissions)</th><th>Pollutant Focus (T/y)</th><th>Proposed Interventions</th><th>Key Inspirations</th></tr></thead><tbody><tr><td>4</td><td>BC = 1.8<br>PM<sub>10</sub> = 6.4<br>PM<sub>2.5</sub> = 9.6<br>CO = 604.3,<br>CO<sub>2</sub> = 4152.1<br>NO<sub>x</sub> = 58.0</td><td>Financial incentives for cleaner fuels, community education on air pollution health impacts.</td><td>Ghana: Rural LPG Programme (subsidies, infrastructure)<br>UK: Clean Air Act (smokeless zones)<br>France: Ban on open burning (composting/ mulching).</td></tr><tr><td>3</td><td>BC = 0.7<br>PM<sub>10</sub> = 7.1<br>PM<sub>2.5</sub> = 6.0<br>CO = 179.7<br>CO<sub>2</sub> = 1670.4</td><td>Subsidies, carbon credits for cleaner fuels, promote LPG and energy-efficient cooking technologies.</td><td>India: Pradhan Mantri Ujjwala Yojana (LPG subsidies)<br>US: Inflation Reduction Act (efficient stove rebates).</td></tr><tr><td>2</td><td>PM<sub>10</sub> = 3.3<br>PM<sub>2.5</sub> = 3.0<br>CO<sub>2</sub> = 808.2</td><td>Expand gas infrastructure, awareness campaigns, integrate air quality education into households.</td><td>Nepal: Clean Cooking Campaign (community education)<br>Sweden: Cleaner wood burning education.</td></tr><tr><td>1</td><td>CO = 413.8<br>CO<sub>2</sub> = 788.5</td><td>Local education campaigns, pilot LPG adoption programs, subsidies with flexible installment payments.</td><td>Cambodia: Subsidized LPG pilot (education, financing)<br>France: MaPrimeRénov' (grants for cleaner systems).</td></tr><tr><td>5</td><td>PM<sub>2.5</sub> = 1.4<br>CO<sub>2</sub> = 195.3<br>NO<sub>x</sub> = 5.9</td><td>Promote affordable gas technologies, develop adapted infrastructure with certifications.</td><td>Uganda: Pay-as-you-go LPG (financing)<br>EU: Ecodesign Regulation (stove emission standards).</td></tr><tr><td colspan="2">Total emissions across five regions (T/y)</td><td></td><td>PM = 88.9<br>BC = 3.5<br>CO = 1375.6<br>CO<sub>2</sub> = 7614<br>NO<sub>x</sub> = 158.2</td></tr></tbody></table>

CO₂ at 1670.44 T/y, proposes subsidies and carbon credits to facilitate the shift to cleaner fuels, broadening LPG access, and a energy efficient cooking technologies. India's Pradhan Mantri Ujjwala Yojana, which provided subsidized LPG connections to millions of low income households, offers a model for expanding access. Similarly, the US Inflation Reduction Act provides rebates of energy efficient electric stoves and cooktops, plus extra benefits for conversions from fossil fuels, incentivizing electrification to cut household emissions. By integrating India's subsidy framework with US style rebates and efficiency standards, Madagascar can use carbon credits to promote sustainable reductions through efficient technologies. Region 2, characterized by PM₁₀ at 3.32 T/y, PM₂.₅ at 3.08 T/y, and CO₂ at 808.28 T/y, recommends initiatives like expanding gas infrastructure, launching targeted awareness campaigns, and integrating air quality education into household routines. Nepal's Clean Cooking Campaign (Jewitt et al., 2022) embeds education into daily practices to drive behavioral changes. Comparably, Sweden's awareness campaign on cleaner wood burning techniques educates households on proper stove use to minimize emissions, reducing health risks from wood stove pollution. By combining Nepal's community focused education with Sweden's targeted combustion guidance an awareness to foster long term CO₂ reductions through behavioral shifts can be managed. Region 1, focusing on CO at 413.83 t/y and CO₂ at 788.56 t/y, suggests local education campaigns, piloting LPG adoption programs, and offering subsidies with flexible installment payments to overcome financial hurdles. Cambodia's subsidized LPG pilot for low income households (Usmani et al., 2017) provides a model for affordability through education and financing. France's MaPrimeRénov'
program, offering grants covering up to 90 % of costs for low income households to replace biomass stoves with cleaner systems like heat pumps, promotes energy efficiency and emission reductions. By scaling Cambodia's pilot approach with France's equitable grant system, accessible pollutant reductions through tailored financing and education can be achieved. Region 5, with PM<sub>2.5</sub> at 1.43 T/y, CO<sub>2</sub> at 195.35 T/y, and NO<sub>x</sub> at 5.97 T/y, proposes promoting affordable gas based technologies and developing context adapted infrastructure. Uganda's Pay-as-you-go LPG model by KopaGas enables affordability through innovative financing. The EU's Ecodesign Regulation (EU) 2015/1185 sets strict efficiency and emission limits on solid fuel stoves, reducing PM<sub>2.5</sub> and NO<sub>x</sub> through certified technologies. Pairing Uganda's financing with EU standards, an enforce certifications on gas adaptations can ensure affordable technologies deliver enduring reductions in vulnerable low emission areas.

By combining developing countries' immediate, accessible strategies with developed nations' regulatory frameworks, incentives, and education, these interventions offer scalable, equitable models for Madagascar to achieve near term emission reductions and long term air quality improvements.

## 6. Conclusion

The present study developed territorial gridded Emission Inventory (EI) combined with Economic Weightage to quantify pollutant emissions for air quality management in low income country focusing on Fianarantsoa, Madagascar. The pollutant emissions evaluated are Black Carbon (BC), Particulate Matter (PM<sub>10</sub>, PM<sub>2.5</sub>), Carbon Monoxide (CO), Carbon Dioxide (CO<sub>2</sub>), and Nitrogen Oxides (NO<sub>x</sub>) from Residential, Hotels and Restaurants (H&R), Open waste burning (OWB) and Transport sectors. This study is based on the preliminary geo localized ground survey (2 % margin of error in 95 % confidence interval level) to identify key sectors, fuel use, and consumption patterns, supplemented by secondary data on demography and land use land cover. Emissions were evaluated for five regions using collected survey data, based on activity levels and emission factors specific to the African context. Regional and sectoral total emissions load for all major sectors was evaluated. Only the residential and H&R sectors were selected for the economic weightage analysis because of insufficient data (other than ground survey data) on the transportation sector. This approach tries to bridge the data gaps often encountered in regional studies and provides a detailed understanding of emission sources and their economic drivers.

Emissions across five regions reveal concerns about localized air quality. The residential activities contribute significantly to air pollution, with 19.09 T/y of PM<sub>2.5</sub> and 13.91 T/y of PM<sub>10</sub>. These values indicate the substantial role residential areas play in PM emissions. Additionally, BC emissions from residential sources stand at 2.52 T/y, CO emissions are also notable, with 463.04 T/y, CO<sub>2</sub> reaches a staggering 6265.07 T/y contributing significantly to the area's carbon footprint and climate impact. Residential NO<sub>x</sub> emissions, although relatively lower at 0.17 T/y, still contribute to regional air quality challenges. H&R contribute less when compared to overall residential emissions but are major contributors especially in regional variations. These activities emit 4.69 T/y of PM<sub>2.5</sub> and 6.73 T/y of PM<sub>10</sub>, BC emissions measured at 0.93 T/y, while CO and CO<sub>2</sub> emissions from H&R amount to 105.94 T/y and 1349.74 T/y, respectively. NO<sub>x</sub> emissions are minimal at 0.04 T/y but still relevant in the overall EI. Vehicular emissions contributing 44.57 T/y of PM, 158 T/y of NO<sub>x</sub>, and 806.7 T/y of CO. OWB adds another 36 T/y of PM. The high density of emissions in this area intensifies pollutant concentration and deposition rates.

The economic weightage analysis along with EI development provides a detailed evaluation of emission loads based on economic weightage, revealing significant regional and sectoral variations that are not captured by traditional top-down emission inventories. Residential activities emerge as the dominant contributor across all regions, with regions 1 and 2 exhibiting exceptionally high weightage for pollutants

like BC (117.7 % and 122 %) and CO<sub>2</sub> (117.6 % and 122 %), driven primarily by residential emissions. Meanwhile, regions 3, 4, and 5 demonstrate a more balanced contribution from both residential and H&R sectors, with region 4 standing out as a PM<sub>10</sub> hotspot due to heightened H&R activity and increased footfall. The dual sector influence of emissions, particularly for PM<sub>10</sub>, where H&R activity plays a significant role in regions 3, 4, and 5 (PM<sub>10</sub>), 33.6 % in region 3, 44.3 % in region 5, and 97.1 % in region 4). For CO and NO<sub>x</sub>, while residential sources dominate, meaningful contributions from the H&R sector in these regions underscore the need for comprehensive, sector specific strategies. Unlike traditional top-down emission inventories, which focus on aggregated emission loads at broader spatial and sectoral scales, this approach delves into sector specific contributions and the economic context of emissions. It enables the pinpointing of local drivers, such as substantial wood consumption in region 2 or high H&R activity in region 4, and highlights regions with exceptionally high economic weightage values and their emission loads. By integrating spatial, sectoral, and economic dimensions, the economic weightage analysis moves beyond quantification to provide a contextual understanding of emissions. This allows for pollutant specific and sectoral evaluations, such as the dominance of residential sources for BC and CO<sub>2</sub> or the dual sector effect of PM<sub>10</sub>. This study captures sector specific contributions across distinct regions, enabling a detailed understanding of localized economic significance on emissions.

While the study presents a detailed sectoral model for territorial emission inventory, it is important to acknowledge the potential uncertainties associated with both the activity data and the EFs applied. The ground survey, though statistically representative, may still be affected by sampling or reporting biases, particularly regarding fuel consumption and informal economic activities. Furthermore, the use of non-local EFs adapted from datasets representing comparable but not identical conditions introduces additional uncertainty. These factors may lead to over or under estimation of certain pollutants depending on sectoral characteristics. Quantifying the magnitude of potential biases arising from uncertainties in activity data (reported fuel use, informal practices) and the use of non-local EFs is not feasible within the present model. This limitation arises because the study area lacks locally measured EFs, controlled combustion efficiency data, that would be required to propagate uncertainties numerically. Nevertheless, the directional tendencies discussed above suggest that the overall patterns and relative sectoral contributions observed provide valuable insights when compared to existing global models. This study fills a significant gap by evaluating emission loads in a Southern African island nation to create a regional EI, delivering high resolution data that highlight significant data gaps in global datasets such as EDGAR and regional studies like DACCIWA. The existing global or regional inventories have almost inadequate information for comparative analysis on a granular scale for localized sources. The obtained results reveal consistent underrepresentation of emissions in global datasets as they provide a broader view. Annual emissions estimates include 3.5 T/y of BC, approximately 3–6 times lower than DACCIWA (10–20 T/y) but nearly 2 times higher than EDGAR (0–2.5 T/y); 88.98 T/y of PM, roughly 4–36 times higher than EDGAR (2.5–25 T/y); 158.2 T/y of NO<sub>x</sub>, about 2–4 times higher than DACCIWA (40–70 T/y) and 6–63 times higher than EDGAR (2.5–25 T/y); 1375.6 T/y of CO, approximately 6–14 times higher than EDGAR (100–250 T/y); and 7614 T/y of CO<sub>2</sub>, about 2–8 times higher than EDGAR (1000–5000 T/y). This study highlights the limitations of relying solely on global datasets like EDGAR for regional planning. Bottom-up approaches, such as the one employed here, offer more accurate baselines of localized sources. This study reveals emission disparities in underrepresented regions and underscores the need for high resolution ground data, opening pathways for future research using cheaper monitoring methods to enhance global inventories and support sustainable development goals.
## CRediT authorship contribution statement

**Rajat Sharma:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Data curation, Conceptualization. **Erwann Rayssac:** Validation, Resources, Formal analysis, Conceptualization. **Andry Razakamanantsoa:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. **Agnès Jullien:** Writing – review & editing, Visualization, Validation, Supervision, Investigation, Formal analysis, Conceptualization.

## Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used Chatgpt for language refinement and enhancing linguistic coherence. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

## Funding

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie COFUND grant agreement No 101034248.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.aeaoa.2025.100399.

## Data availability

Data will be made available on request.

## References

Andrianisa, H.A., Randriatsiferana, F.M., Rakotoson, S.L., Rakotoaritera, F., 2018. Socio-economic integration of the informal recycling sector through an NGO intervention at the andrananitra dumpsite in antananarivo, Madagascar. Waste Manag. Res. 36 (1), 86–96.

Asante, K.P., Afari-Asiedu, S., Abdulai, M.A., Dalaba, M.A., Carrión, D., Dickinson, K.L., Jack, D.W., 2018. Ghana's rural liquefied petroleum gas program scale up: a case study. Energy Sustain. Dev. 46, 94–102.

Ayetor, G.K., Mbonigaba, I., Ampofo, J., Sunnu, A., 2021. Investigating the state of road vehicle emissions in Africa: a case study of Ghana and Rwanda. Transp. Res. Interdiscip. Perspect. 11, 100409.

Borge, R., Santiago, J.L., de la Paz, D., Martín, F., Domingo, J., Valdés, C., Fernández, Á., 2018. Application of a short term air quality action plan in Madrid (Spain) under a high-pollution episode-Part II: assessment from multi-scale modelling. Sci. Total Environ. 635, 1574–1584.

Bourbeau, J., Doiron, D., Biswas, S., Smith, B.M., Benedetti, A., Brook, J.R., Tan, W.C., 2022. Ambient air pollution and dysanapsis: associations with lung function and chronic obstructive pulmonary disease in the Canadian cohort obstructive lung disease study. Am. J. Respir. Crit. Care Med. 206 (1), 44–55.

Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Forouzanfar, M.H., 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015. The lancet 389 (10082), 1907–1918.

EEA, 2025. European environmental agency report. Available: https://www.eea.europa.eu/en/analysis/publications/zero-pollution-monitoring-and-outlook-report.

Elguindi, N., Granier, C., Stavrakou, T., Darras, S., Bauwens, M., Cao, H., Zheng, B., 2022. Analysis of recent anthropogenic surface emissions from bottom-up inventories and top-down estimates: are future emission scenarios valid for the recent past. Authorea Preprints.

EU emission standards, 1992. European emission standards, 2005. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A31998L0069.

Ferm, M., Sjöberg, K., 2015. Concentrations and emission factors for PM2. 5 and PM10 from road traffic in Sweden. Atmos. Environ. 119, 211–219.

Getahun, T., Mengistie, E., Haddis, A., Wasie, F., Alemayehu, E., Dadi, D., Van der Bruggen, B., 2012. Municipal solid waste generation in growing urban areas in Africa: current practices and relation to socioeconomic factors in Jimma, Ethiopia. Environ. Monit. Assess. 184, 6337–6345.

Harivelo, R.Z.M., Harifidy, R.Z., 2022. A review of environmental protection and sustainable development in Madagascar. J 5 (4), 512–531.

Huang, H., Jiang, P., Chen, Y., 2023. Analysis of the social and economic factors influencing PM2. 5 emissions at the city level in China. Sustainability 15 (23), 16335.

Imi, A., 2023. Estimating the demand for informal public transport: evidence from Antananarivo, Madagascar. Public Transport 15 (1), 129–168.

IMF, 2025. The electricity sector and Jirama. https://www.imf.org//-/media/Files/Publ ications/Selected-Issues-Papers/2025/English/SIPEA2025026.ashx.

INSTAT, 2023. Demographic Atlas, troisième recensement général de la population et de l'habitation (RGPH-3). https://www.instat.mg/documents/upload/main/INSTAT_ RGPH-3_ATLAS%20DEMOGRAPHIQUE.pdf.

INSTAT Madagascar, 2018. RGPH-3 demographic atlas of 2018. https://www.instat. mg/p/rgph-3-atlas-demographique-du-rgph-3-de-2018.

Jewitt, S., Smallman-Raynor, M., Binaya, K.C., Robinson, B., Adhikari, P., Evans, C., Hall, I.P., 2022. Domesticating cleaner cookstoves for improved respiratory health: using approaches from the sanitation sector to explore the adoption and sustained use of improved cooking technologies in Nepal. Soc. Sci. Med. 308, 115201.

JIRAMA, 2024. Rapport annuel 2024. https://ccomptes.mg/uploads/RAPPORT-PUBLIC -20241732888483.pdf.

Keita, S., Liousse, C., Assamoi, E.M., Doumbia, T., N'Datchoh, E.T., Gnamien, S., Yoboué, V., 2021. African anthropogenic emissions inventory for gases and particles from 1990 to 2015. Earth Syst. Sci. Data 13 (7), 3691–3705.

Knippertz, P., Coe, H., Chiu, J.C., Evans, M.J., Fink, A.H., Kalthoff, N., Marsham, J.H., 2015. The DACCWA project: dynamics aerosol chemistry cloud interactions in West Africa. Bull. Am. Meteorol. Soc. 96 (9), 1451–1460.

Kuppili, S.K., Alshetty, V.D., Diya, M., Nagendra, S.S., Ramadurai, G., Ramesh, A., De Vito, L., 2021. Characteristics of real-world gaseous exhaust emissions from cars in heterogeneous traffic conditions. Transport. Res. Transport Environ. 95, 102855.

Leon, J.P., Akpo, A.B., Bedou, M., Djossou, J., Bodjrenou, M., Yoboué, V., Liousse, C., 2021. PM 2.5 surface concentrations in southern West African urban areas based on sun photometer and satellite observations. Atmos. Chem. Phys. 21 (3), 1815–1834.

Liousse, C., Assamoi, E., Criqui, P., Granier, C., Rosset, R., 2014. Explosive growth in African combustion emissions from 2005 to 2030. Environ. Res. Lett. 9 (3), 035003.

Mannucci, P.M., Franchini, M., 2017. Health effects of ambient air pollution in developing countries. Int. J. Environ. Res. Publ. Health 14 (9), 1048.

Marais, E.A., Wiedinmyer, C., 2016. Air quality impact of diffuse and inefficient combustion emissions in Africa (DICE-Africa). Environ. Sci. Technol. 50 (19), 10739–10745.

Meghji, J., Mortimer, K., Agusti, A., Allwood, B.W., Asher, I., Bateman, E.D., Marks, G.B., 2021. Improving lung health in low-income and middle-income countries: from challenges to solutions. Lancet 397 (10277), 928–940.

Mencarelli, A., Greco, R., Balzan, S., Grigolato, S., Cavalli, R., 2023. Charcoal-based products combustion: emission profiles, health exposure, and mitigation strategies. Environ. Adv., 100420

Metia, S., Ha, Q.P., Duc, H.N., Scorgie, Y., 2020. Urban air pollution estimation using unscented kalman filtered inverse modeling with scaled monitoring data. Sustain. Cities Soc. 54, 101970.

Ministry of Finance, 2023. Madagascar. https://www.mef.gov.mg/assets/file/echos/ 21344.pdf.

Nwaka, I.D., Nwogu, M.U., Uma, K.E., Ike, G.N., 2020. Agricultural production and CO2 emissions from two sources in the ECOWAS region: new insights from quantile regression and decomposition analysis. Sci. Total Environ. 748, 141329.

OECD, 2020. The economic cost of air pollution. Evidence Europe. Available. https://www.oecd.org/content/dam/oecd/en/publications/reports/2019/12/the-econo mic-cost-of-air-pollution-evidence-from-europe_5629c05f/56119490-en.pdf.

Owili, P.O., Lien, W.H., Muga, M.A., Lin, T.H., 2017. The associations between types of ambient PM2.5 and under-five and maternal mortality in Africa. Int. J. Environ. Res. Publ. Health 14 (4), 359.

Park, J., Kim, H.J., Lee, C.H., Lee, C.H., Lee, H.W., 2021. Impact of long-term exposure to ambient air pollution on the incidence of chronic obstructive pulmonary disease: a systematic review and meta-analysis. Environ. Res. 194, 110703.

Pfotenhauer, D.J., Coffey, E.R., Piedrahita, R., Agao, D., Alirigia, R., Muvandimwe, D., Hannigan, M.P., 2019. Updated emission factors from diffuse combustion sources in Sub-Saharan Africa and their effect on regional emission estimates. Environ. Sci. Technol. 53 (11), 6392–6401.

Pokhrel, R.P., Gordon, J., Fiddler, M.N., Bililign, S., 2021. Determination of emission factors of pollutants from biomass burning of African fuels in laboratory measurements. J. Geophys. Res. Atmos. 126 (20) e2021JD034731.

PSEAU, 2013. https://www.pseau.org/outils/ouvrages/mg_mef_monographie-region-ha ute-matsiatra_2014.pdf.

Qin, L., Wang, M., Zhu, J., Wei, Y., Zhou, X., He, Z., 2021. Towards circular economy through waste to biomass energy in Madagascar. Complexity 2021 (1), 5822568.

Reddy, M.S., Venkataraman, C., 2002. Inventory of aerosol and sulphur dioxide emissions from India. Part II—Biomass combustion. Atmos. Environ. 36 (4), 699–712.

Reliable Prognosis, 2024. Weather in fianarantsoa (2023-2024). https://rp5.ru/Weather _in_Fianarantsoa.
Sarica, T., Chaillou, C., Roustan, Y., Larrieu, C., Wali, S.E., Sartelet, K., 2024. Differentiated impact of low-exhaust-emission vehicles on NO2 and particle concentrations in the paris region. European Transport Res. Rev. 16 (1), 1–12.

Sharma, R., Kumar, A., 2023. Determinants of quantification of tourism waste in the hilly terrain of himalayas: a spatial approach. Waste Manag. 166, 46–57.

Shikwambana, L., Mhangara, P., Kganyago, M., 2021. Assessing the relationship between economic growth and emissions levels in South Africa between 1994 and 2019. Sustainability 13 (5), 2645.

Simayi, M., Shi, Y., Xi, Z., Li, J., Yu, X., Liu, H., Xie, S., 2020. Understanding the sources and spatiotemporal characteristics of VOCs in the Chengdu plain, China, through measurement and emission inventory. Sci. Total Environ. 714, 136692.

Singh, S., Kulshrestha, M.J., Rani, N., Kumar, K., Sharma, C., Aswal, D.K., 2023. An overview of vehicular emission standards. Mapan 38 (1), 241–263.

Sow, I., 2019. Wastes management in African cities. Smart Econ. Smart African Cities: Sustain. Inclusive, Resilient Prosperous 201–218.

Stolz, D., Mkorombindo, T., Schumann, D.M., Agusti, A., Ash, S.Y., Bafadhel, M., Dransfield, M.T., 2022. Towards the elimination of chronic obstructive pulmonary disease: a lancet commission. Lancet 400 (10356), 921–972.

UNEP, 2020. Global sulphur levels [online]. United Nations Environ. Program. Available : https://www.unenvironment.org/global-sulphur-levels.

United Nations, 2015. Transforming Our World: the 2030 Agenda for Sustainable Development. Department of Economic and Social Affairs. Available : https://sdgs.un.org/goals.

Usmani, F., Steele, J., Jeuland, M., 2017. Can economic incentives enhance adoption and use of a household energy technology? Evidence from a pilot study in Cambodia. Environ. Res. Lett. 12 (3), 035009.

Vicente, E.D., Vicente, A., Evtyugina, M., Carvalho, R., Tarelho, L.A., Oduber, F.I., Alves, C., 2018. Particulate and gaseous emissions from charcoal combustion in barbecue grills. Fuel Process. Technol. 176, 296–306.

Wang, S., Gao, S., Li, S., Feng, K., 2020. Strategizing the relation between urbanization and air pollution: empirical evidence from global countries. J. Clean. Prod. 243, 118615.

Wang, X., Firouzkouhi, H., Chow, J.C., Watson, J.G., Carter, W., De Vos, A.S., 2023. Characterization of gas and particle emissions from open burning of household solid waste from South Africa. Atmos. Chem. Phys. 23 (15), 8921–8937.

World Bank, 2023. Madagascar electricity access project (P160442) – Implementation status report. https://projects.worldbank.org/en/projects-operations/project-detail/P160442.

Yang, X., Zhang, T., Zhang, Y., Chen, H., Sang, S., 2021. Global burden of COPD attributable to ambient PM2.5 in 204 countries and territories, 1990 to 2019: a systematic analysis for the global burden of disease study 2019. Sci. Total Environ. 796, 148819.

Zhang, X., Han, L., Wei, H., Tan, X., Zhou, W., Li, W., Qian, Y., 2022. Linking urbanization and air quality together: a review and a perspective on the future sustainable urban development. J. Clean. Prod. 346, 130988.