rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

In the area of public governance, including environmental governance, command-andcontrol approaches, economic or market-based instruments, and public disclosure strategies have been extensively studied and implemented in order to provide incentives for responsible parties to improve their performance, especially in the developed world. Stakeholder dialogue, as an alternative institutional strategy for public governance, has received relatively less attention from researchers and practitioners in developing countries, even though the dialogue strategy can potentially lead to improved outcomes. In the developing world, due to lack of resources, capacity or commitment in pursuing sustainable development, the traditional regulatory approaches are difficult to effectively design or implement. Alternative approaches, such as stakeholder dialogue strategies, have the potential to supplement or complement the traditional approaches.

This paper reports an exploration in China in designing and implementing a stakeholder dialogue strategy – the community environmental roundtables - for promoting both environmentally and socially sustainable development. The rapid economic growth of China in the past three decades has lifted hundreds of millions of people from poverty, but has also generated serious environmental problems with important social implications in the country. The traditional institutional strategies and policy instruments will not be enough for the country to reduce these problems to an acceptable level, at least in the short run. Community stakeholder roundtable dialogue, a bottom-up institutional tool, has been designed and tested in dozens of Chinese municipalities. The experiments have shown that it is feasible to establish such dialogue programs in China, and that the approach is effective in promoting mutual understanding and trust among different stakeholders to improve performance. Positive impacts on institutional transformations are also observed during the process of design and implementation of the stakeholder dialogue strategy.

The next section of this paper will first give a short discussion of stakeholder dialogue as an institutional strategy for environmentally and socially sustainable development. Section III of this paper introduces the Chinese context of public governance, especially environmental governance. The design and implementation of stakeholder dialogue strategies, community environmental roundtables in particular, are presented in section IV, where a number of cases are presented to illustrate the practices and the impacts. A survey of participants on the impacts of roundtable dialogues is reported in section V. Sections VI and VII offer discussions about the dialogue strategy and a conclusion to the paper.

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Figure 1: A ‘rolling’ MTEF and the annual budget process<sup>8</sup> <!-- Start of picture text --> MTEF 2012 – 2015<br>Budget Allocation Forward Estimate Forward Estimate Forward Estimate<br>2012 (t) 2013 (t+1) 2014 (t+2) 2015 (t+3)<br>For approval Estimates on a no policy change basis for three forward years (t+3). For next MTEF<br>Estimates are ‘rolled over’ as indicative allocations for the next (2013-2016)<br>MTEF (2013-2016) by adjusting for new spending approved in the add a new<br>2012 Budget and changes to economic forecasts. The forward year forward year<br>(t+1) becomes the starting allocation (year t) for the 2013 Budget. (t+3)<br>Budget Allocation Forward Estimate Forward Estimate Forward Estimate<br>2013 (t) 2014 (t+1) 2015 (t+2) 2016 (t+3)<br>For approval<br>MTEF 2013 – 2016<br><!-- End of picture text --> In this illustrative MTEF, the ‘no policy change’ basis of the cost projections over the forward years is the level of expenditure required to continue to undertake existing activities, i.e. the ‘baseline’. If there is also an aggregate expenditure ceiling in place over the medium term estimates period, the difference between the ceiling and the baseline is known as ‘fiscal space’ (Schick 2009).<sup>9</sup> It follows that fiscal space can be ‘positive’ (where the available revenue exceeds the baseline) or ‘negative’ (where the baseline exceeds the available revenue). Both cases illustrate the fundamental importance of distinguishing ‘existing’ and ‘new’ programs within an MTEF, as well as the need for systemic capacity to define and cost these programs. In circumstances of positive fiscal space, the budget process rations available resources between new or expanded programs, and in the case of negative fiscal space it becomes a framework for reviewing and reprioritizing existing spending. A high level of integration is required between the annual budget process and the MTEF to make this work (Schick 2009; see also Robinson 2013a).

> 8 Diagram adapted from Overseas Development Institute (2003).

> 9 The concept of ‘fiscal space’ is used here specifically to illustrate the interaction between ceilings and baselines in the rolling estimates mechanism. It can, however, be defined in broader macro-prudential terms, for example as a ‘government’s ability to undertake spending without impairing its solvency, that is, without impairing its present and future ability to service its debt’ (World Bank 2006, 14).

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labor market, or by enabling the selection of an occupation richer in cognitive tasks for those who are about to enter it. Figure 5 shows an increase in the participation of workers for both countries who have completed secondary education, and completed or started tertiary education, to the detriment of those who have not completed secondary education or even primary education.

Figure 5. Distribution of those employed according to their level of education. Years 1995/1998 and 2015 a) Argentina <!-- Start of picture text --> 50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>Up to Sec. Sec. Tertiary Tertiary<br>primary Incomplete Completed Incomplete Completed<br>1998 2015<br><!-- End of picture text -->

# b) Uruguay

<!-- Start of picture text --> 50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>Up to Sec. Sec. Tertiary Tertiary<br>primary Incomplete Completed Incomplete Completed<br>1995 2015<br><!-- End of picture text --> Source: Our own estimation based on household surveys Figure 1.A in the appendix presents the results of the factorial decomposition exercise, segregating by the educational expansion effect described above. The results are convincing. If we subtract the effect due to the increase in educational level of the labor force, the contribution due to the movement of workers between occupations ceases to be significant.

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Deaton, A., and S. Zaidi. “Guidelines for Constructing Consumption Aggregates for Welfare Analysis.” LSMS Working Paper, The World Bank, Washington, DC, 2002. <u>https://openknowledge.worldbank.org/handle/10986/14101</u> Devarajan S., and E. Ianchovichina. “A Broken Social Contract, Not High Inequality, Led to the Arab Spring February.” _Review of Income and Wealth_ 64 (2018): S5-S25.

Esteban, J.-M., and D. Ray. “Conflict and Distribution.” _Journal of Economic Theory_ 87 (1999): 379-415.

Esteban, J.-M., and D. Ray. “Polarization, Fractionalization and Conflict.” _Journal of Peace Research_ 45 (2008): 163-182.

Esteban, J.-M., and D. Ray. “Linking Conflict to Inequality and Polarization.” _The American Economic Review_ 101 (2011): 1345-1374.

Foster, J. E., and M. C. Wolfson. _Polarization and the Decline of the Middle Class: Canada and the US_ . OPHI Working Paper, University of Oxford, Oxford, 1992.

Foster, J. E., and M. C. Wolfson. “Polarization and the Decline of the Middle Class: Canada and the US.” _Journal of Economic Inequality_ 8 (2010): 243-273.

Handcock, M. S., and M. Morris. “Relative Distribution Methods.” _Sociological Methodology_ 28 (1998): 53-97.

Handcock, M. S., and M. Morris. _Relative Distribution Methods in the Social Sciences._ New, York: Springer-Verlag Inc., 1999.

Hassine, N. B. “Economic Inequality in the Arab Region.” _World Development_ 66 (2015): 532556.

Haut Commissariat au Plan (HCP). _Enquête nationale sur la consommation et les dépenses des ménages 2000/2001_ . 2001. <u>https://www.hcp.ma/downloads/Enquete-nationale-sur-laconsommation-et-les-depenses-des-menages_t21181.html</u> Haut Commissariat au Plan (HCP) and World Bank (WB). _Le marché du travail au Maroc : Défis et opportunités_ . 2017a. <u>https://www.hcp.ma/Le-marche-du-travail-au-Maroc-Defis-etopportunites_a2054.html</u> Haut Commissariat au Plan (HCP) and World Bank (WB). _Pauvreté et prospérité partagée au Maroc du troisième millénaire, 2001-2014_ . 2017b. <u>https://www.hcp.ma/Pauvrete-etprosperite-partagee-au-Maroc-du-troisieme-millenaire-2001-2014_a2055.html</u> 36
**Figure 5. Impacts of the carbon tax on national CO2 emissions**

<!-- Start of picture text -->
-4.28<br>-5.46<br>-6.29<br>-7.71<br>-8.35<br>(8.91)<br>-10.00 -9.92 -9.65 -9.92 -9.68 -10.21<br>2a 2b 2c 2d 2e 3a 3c 3d 3e 4c 4e<br>Scenario Scenario 2 Scenario 3 Scenario 4<br>1<br><!-- End of picture text -->

The other interesting finding on the impact of a carbon tax on CO2 emissions is the variations of CO2 reductions between the formal and informal firms (see Table 4). Under Scenario 1, while the formal parts of the economy reduce their CO2 emissions in response to the carbon tax by 10.1%, the informal sectors reduce 3.5%. The reason is that carbon-intensive sectors are mostly formal ones. Under Scenario 2a, 2b and 2c, the CO2 reductions in the formal firms are about the same or smaller than those in informal firms because of the rebound effect (formal firms receive carbon tax revenues to cut their production taxes, whereas informal firms do not). The higher the amounts of carbon tax revenues to cut production taxes in the formal firms, the smaller would be their CO2 mitigation; the reduction of formal sector CO2 emissions under scenario 3c drops to below 2% under Scenario 2c where entire carbon tax revenue (after keeping the government revenue neutral) is recycled to cut production taxes of formal firms including energy firms. These rebound effects are almost eliminated when energy firms are denied the carbon tax revenues to cut their production taxes (Scenario 2d, 2e).

Table 4 also presents overall CO2 reductions associated with the vectors of commodities consumed in each scenario. Percentage emission reductions from the final demand sectors are higher than from the production (intermediate) sectors. This is because households are more sensitive to energy prices than firms. One notable result here is the difference between Scenario 4c and 4e. Under 4e, emission reductions from final demand are 14.5% when VAT of energy commodities are not provided carbon tax revenues to cut their VAT rates. The emission reduction 23
disaggregation of the RHs. In applications with a single RH (like the current one), it projects poverty outcomes on the assumption that distribution does not change.<sup>6</sup>

# **Data**

The database for GEM‐Core consists of a SAM complemented by data related to factor employment, factor and population stocks, elasticities, and a GDP projection. For this macro application, we used the SAM of Table 2.1, expressed in current US dollars for the aggregate of low‐income countries instead of shares of GDP. The role of the SAM is to define base‐year values for the bulk of the model parameters, including those covering production technologies, sources of commodity supplies (domestic output or imports), commodity demands (for household and government consumption, investment, stock change, and exports), transfers between different institutions, and tax rates.

The disaggregation of the rest of the database coincides with that of the SAM; i.e., its size is very modest when GEM‐Core is applied to a two‐sector SAM. The non‐SAM database consists of (a) base‐year government and private employment; (b) base‐year unemployment rate; (c) base‐year private and government capital stocks and depreciation rates; (d) three elasticity values (for transformation of output between exports and domestic sales; and for substitution between labor and capital in private production and between imports and domestic output in domestic demand); and (e) projections for the simulation period for population, labor force participation, and GDP growth. This information is readily accessible from standard sources. Table 2.2 shows a‐d for our application to an archetype low‐income country; Table 2.3 shows e. For instance, we see that the rate of population growth is projected to decline gradually over time, from 2.7 percent in 2016 to 2.4 percent in 2030.

In the base simulation, labor productivity is a free variable, making sure that the projected path of GDP growth is replicated; for the non‐base simulations, GDP growth is endogenous. As discussed below in Section 3, for each simulation, it is also necessary to define (a) equilibrating mechanisms for the government budget, the balance of payments, and the

> 6 GEM‐Core may also feed data to a separate microsimulation module to generate poverty and distributional indicators.

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Thornthwaite (1948) method based on temperature, number of days per month and average monthly day length, and subsequently summed monthly values to obtain annual totals (see, e.g., Willmott, Rowe and Mintz 1985 for details).<sup>17</sup> Figure 4 shows average annual country-level moisture trends for the countries in our sample, indicating the long term downward trend over the last 60 years, consistent with Figure 2. It also shows the high inter-annual variability of moisture in these countries, even with three-year smoothing. The climate data sets have a spatial resolution of 0.5 degrees, which corresponds to about 3000 km<sup>2</sup> at the equator. To generate district level climate indicators, we average grid cell values that overlap with the corresponding sub-national unit, weighting by area in the case of cells that cross district boundaries.<sup>18</sup>

# **5. Empirical analysis of the effect of climate on urbanization**

## **5.1 Specifications**

We estimate the effect of growth in moisture on growth in urbanization for a panel of districts that is highly unbalanced because different countries conduct censuses in different years. Growth rates are annualized to account for these intercensal periods of different lengths. We also estimate a long difference formulation. The base specification is where variables for district i , in country j, in year t , are defined as follows: is annualized growth of the urban population share from to ; is a country-year fixed effect controlling for time-varying national conditions; ; [ ] is average moisture from to ; is the number of years between year t and the prior census; and _Xij_ are time invariant controls, including initial levels of variables.

> 17 More specifically, potential evapotranspiration (PET) for month _i_ is calculated as:

where is the average monthly temperature in degrees Celsius, is the number of days in the month, is day length at the middle of the month, ( ) ( ) ( ) , and the heat index ∑ where indicates the 12 monthly mean temperatures. The Penman method ( ) provides a more precise estimate of PET, but requires data on atmospheric conditions that are not available consistently for the area and time period of this study.

> 18 In practice, we use the number of 0.1-degree sub-cells as a weight.

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**<u>Table 2: Characteristics of the land certification process</u>**

||**Total**|||**La**|**nd type**|||
|---|---|---|---|---|---|---|---|
|||**Collect.**|**Arable**|**Forest**|**Constr.**|**Residential **|**Houses**|
|**Main characteristics**||||||||
|Total area|12,790|7,052|3,295|1,859|343|316|237|
|Titling complete|0.938|0.974|0.979|0.964|0.900|0.911|0.911|
|...if yes, months taken|5.011|4.157|4.455|5.961|5.080|5.330|5.711|
|<br>No. of certificates issued|2,852|95|781|633|393|717|719|
|Area titled (mu)|9,033|5,861|3,159|1,695|306|267|219|
|**Organization and implementation**||||||||
|Rules made by village leaders|0.015|0.019|0.018|0.011|0.017|0.017|0.015|
|Rules made by village representatives|0.127|0.133|0.135|0.121|0.131|0.117|0.124|
|Rules made by village assembly|0.444|0.437|0.465|0.447|0.383|0.438|0.458|
|Rules made by village econ. Organizations|0.225|0.211|0.223|0.232|0.257|0.229|0.215|
|Rules made by township or above|0.188|0.200|0.160|0.189|0.211|0.200|0.189|
|<br>Organization done by village leaders|0.271|0.264|0.276|0.220|0.176|0.270|0.278|
|Organization done by village representatives|0.047|0.048|0.046|0.079|0.049|0.045|0.043|
|Organization done by village assembly|0.129|0.117|0.141|0.126|0.126|0.139|0.141|
|<br>Organization done by village econ. organizations|0.113|0.095|0.131|0.105|0.082|0.127|0.123|
|<br>Organization done by township or above|0.439|0.476|0.406|0.471|0.566|0.418|0.415|
|Actual measurement done by village leaders|0.075|0.074|0.078|0.079|0.088|0.070|0.073|
|Actual measurement by village representatives|0.436|0.415|0.468|0.497|0.337|0.434|0.462|
|Actual measurement done by village assembly|0.041|0.029|0.039|0.026|0.050|0.029|0.047|
|<br>Actual measurement by village econ. organizations|0.289|0.265|0.309|0.330|0.337|0.307|0.298|
|Actual measurement done by township or above|0.159|0.217|0.106|0.068|0.188|0.160|0.120|
|**Disputes**||||||||
|<br>Any disputes encountered|0.502|0.185|0.452|0.314|0.196|0.244|0.243|
|<br>... if yes, no. of disputes|11.102|6.860|9.367|8.883|5.600|7.695|6.652|
|... disputes could not be resolved at village level|0.877|0.104|0.772|0.746|0.029|1.000|0.677|
|**Results**||||||||
|Contract now longer than 30 years|0.959|0.974|0.869|0.968|1.000|0.984|0.989|
|Contract now permanent|0.793|0.844|0.578|0.590|0.910|0.914|0.899|
|<br>**Resources required and costs**||||||||
|Total labor from village (man-days)|1,918|||||||
|Total labor from outside (man-days)|269|||||||
|Total cost (Y/mu)|16.15|||||||
|Share of cost borne byvillage|0.431|||||||

_Source:_ Own computation from 2014 Chengdu village survey based on 529 villages.

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foreign technologies boosts innovation at home and conversely. As an illustration, China is both the top inventor and the top technology importer emerging economy for climate-mitigation technologies (as we will see below in Tables 2 and 3). In fact both strategies improve so-called technological absorptive capacities.

# **2 Innovation and International Technology Diffusion toward the Developing World: Available Evidence**

## **2.1 Innovation through Learning by Searching**

As indicated above, innovation comes through two mechanisms: learning by searching and learning by doing. Patent counts offer an indicator to measure the output of the former<sup>6</sup> . They have been used extensively in recent studies which clearly show that learning-by-searching remains mostly located in the industrialized world. As an illustration, Table 2 shows the share of climate patented inventions by country. China is the only emerging economy in the Top 10. Other major emerging economies or transition countries such as India, Russia or Brazil account for less than 1% of world innovation. Other studies dealing with waste or green chemistry confirm the stylized facts.<sup>7</sup>

**Table 2: Top ten inventor countries in climate innovation and selected emerging economies**

|**Rank**<br>**Country**|**Share of world climate patented**<br>**inventions (2007-2009)* **|
|---|---|
|1<br>USA|19.0%|
|2<br>Germany|18.7%|
|3<br>Japan|17.5%|
|4<br>South Korea|5.6%|
|5<br>France|4.8%|
|6<br>UK|3.6%|
|7<br>Italy|3.4%|
|8<br>Canada|2.7%|
|9<br>China|1.7%|
|10<br>TheNetherlands|1.6%|
|**Total top 10**|**78.6%**|
|18<br>Taiwan,China|0.9%|
|21<br>India|0.7%|
|22<br>Russia|0.5%|
|25<br>Brazil|0.4%|
|31<br>South Africa|0.2%|

Source: Glachant et al. (2013) based on PATSTAT data. * International patents refer here to claimed priorities invented in the country as a share of world claimed priorities. Mean of 25 climate technology shares.

> 6 The proxy is however imperfect for various reasons: First, patents are only one of the means of protecting inventions, along with lead time, industrial secrecy, or purposely complex specifications. Second, the propensity to patent differs between sectors, depending on the nature of the technology. Last, the value of individual patents is heterogeneous. Counting international patent families – inventions that have been patented in at least countries

> – partly mitigates this problem. For an extensive discussion of these limitations, see Johnstone et al. (2011).

> 7 OECD (2011), Invention and Transfer of Environmental Technologies, Paris, OECD.

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Table F.2: Productivity and Technology Sophistication, Controlling for ln(K/L)

|VARIABLES|(1)<br>ln(VAPW)|(2)<br>ln(VAPW)|(3)<br>ln(VAPW)|(4)<br>ln(VAPW)|(5)<br>ln(VAPW)|(6)<br>ln(VAPW)|(7)<br>ln(VAPW)|(8)<br>ln(VAPW)|
|---|---|---|---|---|---|---|---|---|
|ln(K/L)|0.27***<br>(0.04)|0.26***<br>(0.04)|0.26***<br>(0.04)|0.26***<br>(0.04)|0.25***<br>(0.04)|0.25***<br>(0.04)|0.26***<br>(0.04)|0.26***<br>(0.04)|
|ABF EXT|0.49***|2.30***||2.02***|||1.91***||
||(0.07)|(0.33)||(0.38)|||(0.37)||
|ABF EXT<sup>2</sup>||-0.31***||-0.27***|||-0.21***||
|||(0.06)||(0.06)|||(0.06)||
|ABF MOST|||2.34***||1.96***|1.86***||2.08***|
||||(0.50)||(0.51)|(0.50)||(0.49)|
|ABF MOST<sup>2</sup>|||-0.40***||-0.32***|-0.30***||-0.28***|
||||(0.10)||(0.11)|(0.10)||(0.10)|
|ABF GAP|||0.41***||0.32***|0.29**||0.61***|
||||(0.11)||(0.11)|(0.12)||(0.13)|
|Var(ABF EXT)||||0.19*|0.29***|0.91***|1.07**|1.54***|
|||||(0.10)|(0.10)|(0.27)|(0.45)|(0.41)|
|Var(ABF EXT)<sup>2</sup>||||||-0.26***<br>(0.09)|||
|Var(ABF EXT)*ABF EXT|||||||-0.30**<br>(0.14)|-0.43***<br>(0.13)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,519|2,519|2,519|2,519|2,519|2,519|2,519|2,519|
|R-squared|0.53|0.55|0.54|0.55|0.54|0.55|0.55|0.55|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

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