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Source: World Bank estimate. Source: World Bank estimate.

To estimate Equation (1), there are several empirical issues. First, there is potential autocorrelation in the error term _u_ . Our primary source of data is the SPAM, which generates spatial data at the approximately10 x 10 km land area level. Thus, even if the difference variables are taken, it is critical to deal with the possible spatial autocorrelation, i.e.,
##### Cov (  ui,  u j )  0 . By nature, agricultural land is a continuum of various characteristics,

such as soil fertility and water availability. Weather conditions are continuous across near locations. Public infrastructure, such as railway and roads, typically forms a network, which also creates autocorrelation among neighboring areas.

To deal with this problem, the spatial autocorrelation structure is taken into account: ln �� ��∑�� �� ln �� ��� ���ln �� �∑�� �ln ��� �∑�� �ln ��� ��∑�� ���� ��� (4)


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**Table 1. Summary statistics**
Variable Abb. Obs Mean Std. Dev. Min Max Rice produced (ton) _Q_ 3,198 1161.2 3472.4 0.09 120197 Agro-climatic rice productivity (kg/ha) _A_ 3,198 691.6 415.3 0.10 2016 Population who engage in agricultural activities _L_ 3,198 2756.7 6251.8 55.49 210146 Rain-fed land area harvested (ha) _R_ 1,039 179.3 227.7 0.10 1052 Irrigated land area harvested (ha) _I_ 2,527 411.9 565.2 0.90 4908 Fertilizer used (kg) _F_ 3,127 1591.6 2372.3 0.45 22084 Distance to the road network (km) _KM_ 3,198 10.4 9.9 0.00 62 Market Access Index (0 to 1) _MAI_ 3,198 0.061 0.055 0.01 1 Agrobusiness Access Index (0 to 1) All agrobusinesses _AGAI_ 3,198 0.085 0.076 0.01 1 Input dealers and suppliers _AGAI_i_ 3,198 0.076 0.077 0.01 1 Output collectors, processing firms, exporters _AGAI_o_ 3,198 0.089 0.085 0.01 1

**III.** **ESTIMATION RESULTS AND POLICY IMPLICATIONS**

First of all, the ordinary least squares (OLS) regression is performed as a basis for discussion ( **Table 2** ). As discussed above, the results may be biased because of possible autocorrelation in spatial data and excessive zeros in input variables. A small positive number is here used for _R_, _I_ and _F_ in case they are zero. The results are broadly consistent with economic theory: All inputs are productive. While disconnectedness from the road network has a negative effect, proximity to the market and agrobusinesses look important to increase rice production.

Though, one unexpected result is that crop suitability, _A_, has a negative elasticity. It means that farmers might be growing crops in less fertile land, which is not realistic in general.

With the Battese’s specification used, the results are broadly similar to the above ( **Table 3** ).

However, the coefficients of some production inputs have been changed, especially for rainfed land, _R_, which has a number of zeros in our data. The coefficients of irrigation _I_ and fertilizer _F_ also became greater than before, indicating the possible downward bias caused by the small positive number specification. In any case, the OLS estimates are potentially biased without spatial autocorrelation controlled.


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The paper reexamined such a nexus among crop production, transport infrastructure and agrobusinesses. The results show the particular importance of intensification in Malagasy rice production. Fertilizer use has the highest elasticity, followed by irrigation and market accessibility. Consistently, the results indicate the importance of agrobusinesses. Particularly, proximity to input-oriented agrobusinesses, such as input dealers and equipment suppliers, is found to be important: They can facilitate farmers’ adoption of new inputs and technologies.

Methodologically, the paper also attempted to apply newly available data and techniques.

Spatial data are generally rich data sources, which allow to generate various information that would be unavailable or very costly to collect otherwise. The paper combined highly disaggregated crop production data and detailed geo-referenced road network data. Various transport accessibility measurements were developed, which are clearly useful to consider many other policy issues.


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**REFERENCES**

Amaral, Pedro, and Luc Anselin. 2011. Finite sample properties of Moran’s _I_ test for spatial autocorrelation in Tobit models. Working Paper No. 07, GeoDa Center for Geospatial Analysis and Computation, Arizona State University.

Anselin, Luc. 1988. _Spatial Econometrics: Methods and Models._ Kluwer Academic Publishers.

Battese, George. 1997. A note on the estimation of Cobb-Douglas production functions when some explanatory variables have zero values. _Journal of Agricultural Economics_, Vol.

48(2), pp. 250-252.

Bell, Clive and Susanne van Dillen. 2012. How does India’s rural roads program affect the grassroots? Findings from a survey in Orissa. World Bank Policy Research Working Paper No. 6167.

Bravo-Ortega, and Lederman. 2004. Agricultural productivity and its determinants: Revisiting international experiences. _Estudios de Economia_, Vol. 31(2), pp. 133-163.

Damania, Richard, Claudia Berg, Jason Russ, Federico Barra, John Nash, and Rubaba Ali.

2017. Agricultural technology choice and transport, _American Journal of Agricultural_ _Economics_, Vol. 99(1), pp 265-284.

Danida. 2010. Impact Evaluation of Danida Support to Rural Transport Infrastructure in Nicaragua. Evaluation Department, Ministry of Foreign Affairs of Denmark.

Dercon, Stefan, Daniel Gilligan, John Hoddinott, and Tassew Woldehanna. 2008. The impact of agricultural extension and roads on poverty and consumption growth in fifteen Ethiopian villages. IFPRE Discussion Paper 00840.

Dillon, Andrew. 2011. Do differences in the scale of irrigation projects generate different impacts on poverty and production? _Journal of Agricultural Economics_, Vol. 62(2), pp.

474-492.

Donaldson, Dave. 2010. Railroads and the Raj: The economic impact of transportation infrastructure. NBER Working Paper No. 16487.

Dorosh, Wang, You, and Schmidt. 2012. Road connectivity, population, and crop production in Sub-Saharan Africa. _Agricultural Economics_, Vol. 43, pp. 89-103


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predictions of CDM diffusion are consistent with the historic pattern of growth in CDM projects and behave according to conceptual models of technology diffusion. We fit a sigmoid expansion path model to historic CDM expansion data and test whether the predicted size of the CDM market will be exceeded during the first commitment period of the Kyoto Protocol and beyond. Estimates of the future size of the CDM market are of paramount importance to investors and policy makers as both groups are concerned with the attractiveness of the CDM mechanism. One of the questions discussed in the UNFCCC Conference of the Parties (COP) in Copenhagen in December 2009 is whether or not CDM should remain one of the major mechanisms to allow countries reduce the cost of meeting their emission reduction targets. The answer to such question may rely heavily on the trends of the CDM market, as predicted in our analysis.

The remainder of the paper is organized as follows. The next section presents a brief review of relevant literature on diffusion of technologies. Section three examines the mitigation potential of the CDM and presents the available estimates of the size of the CDM market. Section four describes the CDM pipeline data and presents the empirical results for the CDM diffusion pattern along with projections of CDM activity during and beyond the first commitment period of the Kyoto Protocol. The last section discusses the policy implications, indicates areas of future research, and concludes.

**Technology Diffusion Literature**

Various models of diffusion have been developed to explain changing populations, technology diffusion, and adoption of new consumer products. All of those models are founded on theories concerning the spread of information either through interactions between adopters and non-adopters or through exogenous sources (Feder and Umali, 1993).

Aggregate models on technology diffusion are founded upon the epidemic or logistic model [7] . The logistic model views the diffusion process to be similar to the spread of an infectious disease, with the analogy that contact with other adopters (i.e., learning from the experience of others) and exposure to information on the innovation (i.e., demonstration effect) leads to adoption. The model is based on the assumption that members of a 7 See, for example, Griliches (1957, 1980), Mansfield (1961), Gore and Lavaraj (1987), Doessel and Strong(1991), Knudson (1991) and Danar and Yaron (1992).

4

CDM pipeline data
historic CDM expansion data


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Summary statistics for the sample are given in table 4. The first projects entered the pipeline in December, 2003 and the sample period ends in our analysis in August 2009, so the first and last rows of the table represent results from partial years. The table gives the number of projects entering the pipeline each year, the additional CERs the new projects are expected to provide annually by 2012 and by 2020, and the accumulated stock of annual project CERs for both periods. [10] A quick examination of the table reveals that the expected flow of CERs from the pipeline corresponds to the high end of early predictions of market size reported in table 3. The table also shows that the number of projects has grown each year, although the rate of growth stalled slightly in 2008. Not all projects that enter the pipeline are successful; some projects are withdrawn by the project developer or rejected by the Board. The last column of the table indicates that about 3-14 percent of the 2012 CERs entering the pipeline are withdrawn or rejected. Averaging masks some of the variation in the scale and frequency with which CERs entered the pipeline. Figure 1 shows the build-up of the introduction of new projects and the stock of CERs against a daily scale.

Applied diffusions models As discussed, the purpose of this paper is to estimate the CDM diffusion patterns and compare ex ante estimates of CDM market size with predictions from the diffusion models.

In particular, we use information about projects entering the CDM pipeline and combine it with an assumption that the adoption of CDM project methodologies follows a path typical of new technologies. We consider two types of models, a symmetric and an asymmetric model.

Following Feder and Umali (1993), the general form of the logistic model for CDM adoption is _dcdtt_  ~~~~ _cc_ - _t_ ( _c_ -  _ct_ ), (1) where, in our case, _ct_ denotes the accumulated expected flow of amount of CO2 to be abated through existing CDM pipeline projects at time _t_, and where _c_ - denotes the overall saturation point, and where _β_ is a parameter measuring the rate of adoption. With time, the 10 The UNEP/Risoe data reports total CERs by 2012 and 2020. Annual values are simply these total divided by 5 and 10 respectively. This is done to correspond to model projects, which, by convention, are stated as CERs produced annually.

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spread of CDM markets is expected to slow and reach a steady-state equilibrium. This results in a sigmoid cumulative density function over time. The logistic model thus imposes a symmetric S-shaped adoption trend.

Solving the logistic differential equation (1) for _ct_ yields the standard logistic growth function _m_ _ct_  1(  exp[(  _b_ 0  _b_ 1 ( _t_  _t_ 0 )], (2) where _[c]_ _t_ [ is the annual flow of CERs implied by the projects that have accumulated in the ] CDM pipeline from the first project in _[t]_ 0 [ to time ] _[t]_ [ . There are three parameters that ] determine the shape of the function: _b0_ is the coefficient of integration, which shifts the location (intercept) of the function without affecting its shape; _b1_ is a coefficient representing the rate of adoption over time; and _m_, which is an estimate of the saturation point, _c_ _[*]_ .

Conveniently, these parameters can be directly estimated from a time-series of accumulated CERs implied by the pipeline projects using a non-linear estimator. Moreover, tests of the reasonableness of previous forecasts about the eventual size of the CDM market can be tested by imposing those values on the saturation parameter, _m_ .

While the parameters of the functional form given in equation 2 are easy to interpret and the form itself is convenient for estimation, a defining feature of the specification is that the underlying cumulative distribution is symmetric and implies that the maximum adoption rate occurs at a point half-way between zero and the saturation level. This imposes an arbitrary inflection point on the trajectory, which, in turn, has encouraged researchers to use more flexible forms. [11] Among these, one of the more flexible specifications is the four parameter Gompertz function, which we estimate as an alternative. The form traces out an asymmetric sigmoid shape and is written out as:  _b_  _me_  _b_ 1 _e_  _b_ 2 ( _t_  _t_ 0 ) (3) _ct_  _b_ 0  _me_  _b_ 1 _e_  _b_ 2 ( _t_  _t_ 0  _me_  _b_ 1 _e_  _b_ 2 ( _t_  _t_ 0 ) where, as before, _b0_ shifts the location of the function without affecting its shape, _m_ is an estimate of the saturation point and where _b1_ and _b2_ together determine the adoption rate.

11 See Michalakelis, Varoutas and Sphicopoulos (2008) for a detailed discussion of alternative applied diffusion models.

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As discussed, recent pipeline data already indicates that expected CER flows from pipeline projects exceed ex ante model predictions. From model parameters, it is possible to construct confidence intervals around each observation. Using this approach, we calculated the probability that the cumulative number of CERs identified in August 2009 already exceed the upper limit of 520 million tons of carbon dioxide equivalent (mtCO2e) predicted by the ex ante model. Based on parameters from the symmetric model, there is a 90 percent probability that the cumulative level of CERs in the August pipeline top the upper range of the model predictions for 2010. Using the better-fitting asymmetric model parameters, the probability exceeds 99 percent. Similarly, a test that the upper bound is really the 520 mtCO2e is rejected in both models. [15] It is likely that not all projects in the pipeline will succeed in producing the full complement of credits predicted in documents submitted to the CDM Board and some of the credits may arrive after the close of the first commitment period. Moreover, there remains some skepticism in the environmental community about whether leakage and inexactness in project baselines dilute the environmental integrity of the mitigation effects measured by the CDM. Even so, barring a significant departure from current trajectories, the CDM appears on track to match or exceed early expectations when the program was begun.

**Summary and Conclusion**

There is a consensus view that, at reasonable prices for carbon, most of the global opportunities for mitigation reside in developing countries. And, to date, the Clean Development Mechanism is the only formal way developed countries can access mitigation opportunities in developing countries to meet their commitments under the Kyoto Protocol.

Additionally, recent estimates by the IPCC suggest that a significant portion of the mitigation opportunities in developing countries are in agriculture, forestry and improving building efficiency. Yet, so far, project investments under the CDM are narrowly focused on the energy and industrial sectors, leaving these broad areas of potential untouched. This differs from earlier assumptions of how the program would work and raises the question of whether mitigation opportunities that the CDM currently taps will be sufficient to meet 15 The test is based on setting _m_ in (2) and (3) equal to 520.

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**Table 1. Estimated Economic Potential for Mitigation in 2030 under**
**Alternative Price Scenarios, Giga tons of carbon dioxide equivalent**
**(GtCO2e)**
Mitigation Sources Low prices High prices Global 15.80 31.10 OECD countries 4.90 7.40 Transition economies 1.80 2.80 Developing countries 8.30 16.80 Developing country sector Energy supply 1.30 2.70 Transport 0.15 0.15 Buildings 2.70 3.30 Industry 1.60 3.80 Agriculture 1.60 4.50 Forestry 0.75 3.00 Waste 0.20 0.70 Source: Table 11.3 in chapter 11 of IPCC Working Group III [24] 14


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**Table 2. CDM Pipeline Inventory of Projects, by Project Type**

Annual CERs Annual CERs Number of Projects 2008-2012 2008-2020 4,631 2,786,791 7,416,430 Project type Share of Total (%) Afforestation 0.11 0.01 0.18 Agriculture 0.02 0.01 0.01 Biomass energy 14.29 7.02 6.49 Cement 0.63 1.13 0.79 CO2 capture 0.06 0.01 0.01 Coal bed/mine methane 1.45 4.62 4.71 EE households 0.45 0.14 0.14 EE industry 3.13 0.78 0.61 EE own generation 9.78 9.27 8.68 EE service 0.37 0.03 0.03 EE supply side 1.32 1.39 2.70 Energy distribution 0.19 0.35 0.34 Fossil fuel switch 2.33 6.37 6.13 Fugitive gas 0.50 1.92 1.66 Geothermal 0.32 0.60 0.58 HFCs 0.48 17.10 14.83 Hydro 26.97 17.23 20.75 Landfill gas 5.94 7.58 6.78 Methane avoidance 11.34 4.25 3.73 N2O 1.45 8.95 8.44 PFCs and SF6 0.30 0.46 0.54 Reforestation 0.95 0.49 0.81 Solar 0.78 0.08 0.10 Tidal 0.02 0.04 0.05 Transport 0.26 0.18 0.18 Wind 16.56 10.00 10.75 Source: Based on UNEP Risoe CDM/JI Pipeline Analysis and Database, September 01, 2009.

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**Table 3. Estimates of the Potential Demand for Emissions Reductions and Size of the CDM**
**Market by 2010.**
Annex B Countries' Demand for Kyoto Units Potential Size of the under the Protocol CDM Market Study mtCO2e/year mtCO2e/year Blanchard, Criqui, and Kitous [ a] (2002) 688−862 0−174 Eyckmans et al. [ a] (2001) 1,414−1,713 261−499 Grutter [ a] (2001) 1,000−1,500 0−500 Haites [b] (2004) 600−1,150 50−500 Halsnæs [b] (2002) 600−1,300 400-520 Holtsmark [b] (2003) 1,246−1,404 0−379 Jotjo and Michaelowa [ a] (2002) 1,040 0−465 Van der Mensbrugghe (1998) 1,298 397 Vrolijk [b] (2000) 640−1,484 300−500 Zhang [b] (1999) 621 132−358 Range 600−1,713 0−520 Note: [a] The forecasts assume that only the United States does not ratify the Kyoto Protocol; [b] the forecasts assume that Australia and the United States do not ratify the Kyoto Protocol. Sources: Haites (2004) and Zhang (1999).

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particular, it can be optimal to implement more expensive measures before cheaper ones, if the former have a large potential but are long to implement.
Moreover, focusing on short-term targets (e.g., for 2030) without considering longer-term objectives (e.g., for 2050 and beyond) could lead to carbon-intensive lock-ins that would make it much more expensive (and potentially impossible) to achieve the long-term objectives.
However, MAC curves do not report information on technical inertia, and thus look like abatement supply curves (e.g. DECC, 2011, fig 17). If MAC curves were supply curves, the optimal emission-reduction strategy would be to “implement the cheapest measure first, preferring measures with a lower total saving potential but more cost-effective than those with a higher GHG saving potential in absolute terms” (W ̈achter, 2013). To avoid this, Vogt-Schilb and Hallegatte (2014) propose to complement MAC curves with information on implementation speeds, and to use a simple optimization tool to derive optimal strategies taking inertia into account.
In this paper, we test these theoretical findings on a MAC curve built at the World Bank for studying low-carbon development in Brazil in the 20102030 period, and propose improvements to MAC curves. Lack of data beyond 2030 does not allow us to demonstrate that using the 2010-2030 MAC curve to design a mitigation strategy would lead to suboptimal choices in view of longerterm objectives (2050 and beyond). We can however illustrate this problem by assuming that we want to achieve an objective for 2030, and that we use the MAC curve to design a mitigation strategy for the 2010-2020 period only.
We find indeed that a strategy for 2010-2020 that disregards the 2030 target under-invests in options such as metro and other transportation infrastructure.
Conversely, it over-invests in marginal, cheap but low-potential options, such as heat integration and other improvements in existing refineries.
This work confirms two limits of current MAC curves.
First, MAC curves do not report a very important piece of information, namely the implementation speed of each measure and option. We show however that with marginal modifications, MAC curves could mitigate this problem and better inform decision makers on optimal mitigation strategies. To do so, MAC curves can easily be complemented with information on the speed at which a measure produces abatement results. We propose in Appendix B some guidance for the experts in charge of collecting the information to build a MAC curve, to make sure constraints on implementation speed are collected and reported together with data on costs and potentials. Also, existing and future MAC curves could be presented together with the corresponding emission reduction scenarios or wedge curves (Pacala and Socolow, 2004; Davis et al., 2013), making the dynamic component on the mitigation scenarios more explicit (Fig. 2).
Second, MAC curves are designed for a relatively short term horizon (e.g., 2030), while mitigation objectives go way beyond this horizon. Many countries have longer-term objectives (e.g., the EU has a 2050 objective); and most importantly, tackling climate change and other environmental threats will require to reduce emissions to near-zero levels by the end of the century (e.g., Matthews and Caldeira, 2008; Steinacher et al., 2013). There is no easy way around this problem. Following World Bank (2012, p. 153), we suggest to combine a “synergy approach” based on MAC curves (to identify mitigation options that provide co-benefits in terms of development, economic growth, job creation, local environmental quality, or poverty alleviation) with an “urgency 3


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Marginal cost $/tCO2 T time Wedge curve Flipped MAC curve Figure 2: By displaying “flipped” achievable-potential MAC curves nearby the corresponding emission reduction scenarios (wedge curves), confusion on how to interpret MAC curves may be avoided.

approach”, based on defining long-term objectives and working backward to identify which measures are needed early to get there on time. In particular, sector-specific policies may be used to ensure global short-term targets are met without under-investing in the ambitious and long-to-implement abatement measures required to achieve otherwise-difficult-to-enforce long-term targets (Hallegatte et al., 2013).
The rest of the paper is structured as follows. In section 1, we review different types of MAC curves. While the construction of MAC curves sometimes requires to investigate the diffusion speed of emission-reduction options, MAC curves do not report separately the long-term abatement potential and the diffusion speed. In section 2, we reanalyze the data from the Brazilian MAC curve and confirm our theoretical results. We also propose a simple optimization model that can be used with this information to compute the least-cost emissionreduction schedule. In section 3, we conclude and formulate recommendations on the gathering and reporting of data to inform the policy debate on emissionreduction options.

1. Existing MAC curves

Measure-explicit MAC curves have been developed since the early 1990s (Rubin et al., 1992), and have reached a wide public after McKinsey and Company published assessments of the cost of abatement potentials in the United States (Enkvist et al., 2007) and at the global scale (McKinsey, 2007). This type of curve is increasingly used to inform policy makers. For instance, McKinsey currently lists MAC curves for 15 different countries or regions on its website.
The World Bank also uses MAC curves routinely (ESMAP, 2012), and has recently developed the MACTool to help build such MAC curves (see below).
Similar depictions have been used by other institutions (e.g., Climate Works Australia, 2010; NERA and Bloomberg, 2011; CE Delft, 2012) and to analyze other climate-change related topics, such as waste reduction, energy savings and water savings (see Kesicki and Ekins, 2012; Vogt-Schilb and Hallegatte, 2014).
Depending on their implicit definition of the abating potential of a measure, 4


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The MAC curve we reanalyze in this paper is an achievable-potential MAC curve. In each economic sector, emission reduction scenarios have been assessed taking into account implementation barriers (de Gouvello, 2010).

1.3. MAC curves at the World Bank: MACTool

The World Bank develops and promotes a piece of software called MACTool, which can produce achievable-potential MAC curves. One aim of the MACtool is to provide policy makers with a common framework to analyze available mitigation measures. MACTool could become a reference, notably on what is the relevant information that a decision maker requires to take action regarding mitigation plans. MACTool takes as inputs the key socio-technical parameters of a set of large mitigation measures, and macroeconomic variables. For instance, technology options to produce electricity are characterized by required capital and operation expenditures, as well as their lifetime, energy efficiency and type of fuel used. Physical constants as the carbon intensity of each fuel are factored in. The user must also specify at least one scenario on the future macroeconomic variables of interest, such as the price of fossil fuels and the future demand for electricity. Finally, the user must provide scenarios of future penetration of (low-carbon) technologies and measures, in both a baseline and at least one emission-reduction scenario (ESMAP, 2014).
As outputs, MACTool computes the amount of GHG saved by each measure in the long run (in MtCO2), and the cost of doing so (in $/tCO2). This information is illustrated with two figures: an achievable potential MAC curve, and an abatement wedge curve.
The tool itself does not provide information on what is achievable, this information comes directly from the input scenarios. Input scenarios therefore need to be built taking into account the constraints on technology diffusion and implementation speed. For instance, these scenarios may come from models that factor such constraints in, or be built by sector experts who guesstimate possible penetration scenarios (see also Kesicki and Ekins, 2012).
MACTool also reports the investment needed in different emission reduction scenarios. While the abatement costs are computed using the social discount rate entered by the user, MACTool can also compute the carbon price signal that would be required to trigger investments from the private sector, taking into account the opportunity cost of capital for private actors (that is higher than the social discount rate, especially in developing countries).

2. Proof of concept: Re-analyzing the case of Brazil by 2030 In a theoretical framework, Vogt-Schilb and Hallegatte (2014) find that focusing on short-term targets and disregarding constraints on implementation speed would lead to suboptimal strategies, making the longer-term target more expensive to reach. In some cases, doing so would even lead to a carbon-intensive lock-ins, making the longer-term target impossible to reach. They develop a simple optimization model to factor implementation speed in the analysis and avoid this problem.
Here, we perform a proof of concept for these ideas, reanalyzing the data used at the World Bank to create a MAC curve for Brazil with MACTool (de Gouvello, 2010). We test the optimization model using proxies and indirect methods 6


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Figure 3: In the data from MACTool, many emission reduction scenarios (+) may be approximated by a piecewise-linear function (red curve). The slope of the first piece provides the diffusion speed for that measure. The second part is interpreted as the maximum potential, that grows over time.

to reconstruct data on implementation speed constraints. Our results confirm the findings of the theoretical paper.

2.1. Objective We take the point of view of a social planner who chooses in 2010 an emissionreduction schedule to comply with an emission target for 2030. We use two simulations.
In the first one, an emission-reduction target is set for year 2030 and an optimal emission strategy is derived, taking into account implementation speed constraints. Then, the abatement obtained in 2020 in this optimal strategy is used as a target for 2020, and the MAC curve is used to design a mitigation strategy between 2010 and 2020, disregarding the longer-term objective.
We then investigate differences of the optimal emission reductions up to 2020 in the two simulations, and confirm the theoretical findings: because of technical inertia, using a MAC curve without taking into account long-term objectives would lead to insufficient short-term investments in options with high potential and slow implementation speed.

2.2. Methods and data We use a spreadsheet program based on the model proposed by Vogt-Schilb and Hallegatte (2014). The program provides the least-cost emission-reduction schedule that complies with the abatement target. As inputs, it requires a list of measures, characterized by a marginal abatement cost, a maximum diffusion speed, and a maximum abatement potential (see Appendix A).
Note that the abatement potential may evolve through time. For instance, if available technology limits intermittent wind power to 20% of the electricity production and electricity production is expected to grow over time, then the abating potential of wind power grows over time. On the other hand, if natural resources provide only few opportunities to build dams, the abating potential of hydro power is fixed, regardless of total electricity demand growth. We thus extend the model by Vogt-Schilb and Hallegatte (2014) to allow for growing abatement potentials (see below and Appendix A).

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(a) No potential (b) No speed Figure 4: For some abatement measures, the data needed to calibrate our model cannot be derived from the emission-reduction scenarios provided in MACTool. In some cases (a), the long term potential is not binding, because it cannot be reached before 2030. In some other cases (b), the diffusion speed cannot be assessed (either it was not investigated, or the measure can reach its full potential in less than one year).

We use data collected at the World Bank to build a MAC curve (using MACTool) during a case study on Brazil (de Gouvello, 2010). The MAC curve provides a list of emission-reduction measures, their marginal abatement cost, and the potential achievable by 2030.
While the list of measures and their cost can be used directly in our spreadsheet program (see the first two columns of Tab. 1), our program requires the full-abatement potential and diffusion speed. Since the diffusion speed and the full-abatement potential were not reported separately, we have to reconstruct it with indirect methods, using the emission-reduction scenarios that were provided to MACTool. For each measure, the shape of the emission-reduction scenarios can be classified in one out of three cases.
In the first case, emission-reduction scenarios may be approximated by a two-phases piecewise-linear function as in Fig. 3. In this case, the diffusion speed is given by the slope of the first piece, and the second phase is interpreted as the growth in full potential. About half the measures fall in this category.
Other emission-reduction scenarios may be approximated by a single linear diffusion (Fig. 4a). In this case, the full potential is not binding before 2030.
We calibrate the diffusion speed from the slope of the penetration pathway, and denote the lack of data on the full potential with a dot (·) in the two last columns of Tab. 1.
In some cases the emission-reduction scenario lacks the first phase; abatement immediately “jumps” to a growing full-potential (Fig. 4b). We denoted them with a dot in the diffusion speed column in Tab. 1. There is usually a handful of such cases in MAC curves exercises. One example from the Brazilian study is solid residues management. In the emission-reduction scenario, solid residues management is able to reduce emissions by more than 40 MtCO2 in one year, and then grow at less than 1 MtCO2/yr. From the perspective of the user of a MAC curve, it may be unclear whether this should be considered as a shortcoming in the data (if the investigation could not identify the constraints that limit the diffusion of solid residues management), or a realistic emission 9


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reduction scenario (if solid residues management can actually save lots of GHG in a short time lapse). [3] To avoid this situation in the future, we recommend that the terms of reference for the experts in charge of collecting data on emission reductions options should explicitly ask to report possible diffusion speeds (Appendix B).
Finally, some emission-reduction measures (reforestation, air conditioning and rapid bus transit) were included in the list while lacking either a marginal abatement cost or an emission scenario. These measures, as well as those for which the diffusion speed could not be estimated, are discarded for the rest of the analysis. The remaining options allow to reduce Brazilian emissions in 2030 by 223 MtCO2 (compared with 812 MtCO2 in the original MAC curve).

2.3. Optimal strategy with a 2030 objective In a first simulation, we run our spreadsheet model to design the optimal strategy to achieve 223 MtCO2 of emission reductions by 2030 (Tab. 2). The optimal emission-reduction strategy has the following characteristics.
All negative-cost measures are introduced at full speed from year 2010, independently of the emission-reduction target. This simply reflects that these measures are desirable per se, as they bring more benefits than costs even in the absence of any carbon pricing or climate change impacts.
For the positive-cost measures, the least-cost strategy is to implement them as late as possible, to benefit from the discount rate. This means that under an emission-reduction target in one point in time, such as -30% by 2030, the twophase penetration pictured in Fig. 3 is not optimal for positive-cost measures. A better solution is to delay the implementation such that the maximum potential is reached just in time, when the target needs to be achieved. [4] 2.4. Strategy with a 2020 objective The optimal emission reduction pathway to achieve 223 MtCO2 in 2030 leads to 127 MtCO2 of emission reductions in 2020 (Tab. 2). To investigate how focusing on short-term targets may lead to suboptimal outcomes, we run a second simulation with the only constraint of reducing emissions by 127 MtCO2 in 2020, and we investigate the “optimal” solution provided by our model in this case (Tab. 3). In line with Vogt-Schilb and Hallegatte (2014), the least-cost strategy for 2010-2020 uses different emission-reduction options, depending on whether the strategy aims at a short-term target (127 MtCO2 in 2020) or at a longerterm one (223 MtCO2 in 2030). The simulation that ends in 2020 uses notably less investment in metro and other clean transportation infrastructure, and more 3 Livestock and forest management is a particular example. In the emission-reduction scenarios, this measures allows to save 229 MtCO2, that is almost one third of the total abatement potential by 2030, as soon as 2010. Since Brazil has already managed to reduce drastically its emissions from deforestation (-80% between 2004 and 2009), the study considered that this mitigation option is already enforced. Sustaining such effort over a long period will require to ensure that productivity gains in the livestock sector free-up pasture land fast enough to accommodate the growth of the livestock-agriculture sector without deforesting, as recommended in the Brazil Low-carbon study (de Gouvello, 2010). Such dissemination will face a similar inertia issue; however, this is a slightly different discussion.
4 If the climate mitigation target was expressed in terms of a carbon budget (consistently with climate change physics, Zickfeld et al., 2009), then the two-phase penetration target would be optimal (Vogt-Schilb and Hallegatte, 2014, section 4).

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Ha-Duong, M., Grubb, M., Hourcade, J., 1997. Influence of socioeconomic inertia and uncertainty on optimal CO2-emission abatement. Nature 390 (6657), 270–273.

Hallegatte, S., Fay, M., Vogt-Schilb, A., 2013. Green industrial policies: When and how. World Bank Policy Research Working Paper (6677).

Jenkins, J. D., 2014. Political economy constraints on carbon pricing policies: What are the implications for economic efficiency, environmental efficacy, and climate policy design? Energy Policy.

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Climate Policy 12 (2), 219–236.

Kopp, A., Block, R. I., Iimi, A., May 2013. Turning the Right Corner: Ensuring Development through a Low-Carbon Transport Sector. The World Bank.

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Lecuyer, O., Quirion, P., 2013. Can uncertainty justify overlapping policy instruments to mitigate emissions? Ecological Economics 93, 177–191.

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W ̈achter, P., 2013. The usefulness of marginal CO2-e abatement cost curves in Austria. Energy Policy 61, 1116–1126.

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Zickfeld, K., Eby, M., Matthews, H. D., Weaver, A. J., 2009. Setting cumulative emissions targets to reduce the risk of dangerous climate change. Proceedings of the National Academy of Sciences 106 (38), 16129–16134.

Appendix A. Model We extend the model proposed by Vogt-Schilb and Hallegatte (2014).
The quantity of emission reductions done with measure i at time t is denoted ai,t. The abatement potential of measure i is denoted Ai,t, imposing the following constraint: [6] ∀(i, t), ai,t ≤ Ai,t (A.1) The second constraint on emission reduction is that they cannot grow faster than the diffusion speed vi, such that: ai,t+1 ≤ ai,t + vi (A.2) The abatement target, set for a date in the future T (e.g. 2020 or 2030), is denoted a [⋆] T [.] [It] [sets] [the] [following] [constraint:] ``` � ``` ai,T ≥ a [⋆] T (A.3) i Taking the discount rate r as given, the model computes the least-cost emission reduction schedule, where the cost of measure i is ci: min ai,t ``` � ``` e [−][rt] ci ai,t (A.4) i,t An Excel implementation of this model is available online.

Appendix B. Information collection guidance The following proposes guidance on how data on emission reduction measures could be collected to take into account the findings of this paper. The objective is to collect data that can be used to build MAC curves as usual, and also to inform a prescriptive model. Asking specifically to disclosure assumptions 6 In the model proposed by Vogt-Schilb and Hallegatte (2014), abatement potentials do not evolve over time. This is the only extension we propose.

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4

## **1**
##### **Executive Summary**

This report provides information on incidents of
Gender-Based Violence (GBV) reported by survivors in
Jordan during 2021. The information was gathered with
the consent of survivors who received psycho-social
support (through the case management approach)
via seven organizations members of the Gender Based
Violence Information Management System (GBV
IMS) Taskforce. The GBV IMS Task Force [1] is the body
responsible for gathering, maintaining and analyzing
data related to GBV, along with ensuring the security
and protection of sensitive data concerning GBV. The
Task Force is also responsible for drafting reports and
providing strategic directions to GBV programmes
based on identified gaps and trends.
It is important to highlight that the data and trends noted
in this report are not representative of the prevalence
of GBV in Jordan (or among refugee populations) as
these trends are based solely on incidents reported by
survivors to the Data Gathering Organizations (DGOs) [2]
engaged in GBV response and using the GBV IMS in
2021. It is accordingly not advisable to use these
findings as a proxy for the prevalence of GBV in any
settings or to use it in isolation to monitor the quality
of programmatic interventions. Despite the above
limitations, the GBV IMS is considered the highest
quality GBV incident data currently available to the
humanitarian actors, which can be used effectively
for trend analysis and improving coordination of GBV
prevention and response.
The number of survivors assisted by members of the
GBV IMS Task force in **2021 increased by (19.4%)**
**after a decrease that was observed in 2020.** This
can be explained by the resumption of face to face
service provision in all sites during 2021, including in
person activities which areconsidered an important
entry point for women and girls. Additionally, remote
service provision was maintained which continued to
offer a feasible option for those who could not come
to the centers. Because of the changes in government
measures and easing of movement restrictions to
contain the COVID 19 pandemic, GBV incidents
reported increased rapidly following the re-opening of
service delivery centers. Survivors had also options of
hotlines and phone based case management.

1 The Gender-based violence Information management system
(GBVIMS) Task Force members have signed an Information Sharing
Protocol that defines roles and responsibilities and data protection
procedures. The Taskforce is chaired by UNHCR and UNFPA with the
technical support of UNICEF.
2 INTERSOS, Jordanian Women Union (JWU), Noor Al Hussain
Foundation (NHF), Jordan River Foundation (JRF), International Rescue
Committee (IRC), Arab Women Organization (AWO) and United
Nation High Commissioner for Refugees (UNHCR).

Moreover, the increase could also be linked to cash
assistance offered by some of the DGOs which
contributed to increased access to services.
In terms of nationalities of survivors seeking help:
66% are Syrians, 27.4% are Jordanian and 6.5% are
survivors of other nationalities mainly Iraqis, Yemenis
and Sudanese. It is important to mention that 2021
has marked a slight increase in the percentages of
Jordanian survivors assisted by members of the GBV
IMS task force (3% increase compared to 2020), this
could be due to a higher number of awareness programs
on availability of services targeting Jordanians and
strengthened partnerships between the DGOs and
local CBOs.
Finally, it is also important to stress that in line with the
last five years the majority of survivors reached services
more than one month after the incident. In 2021 59.6%
reached them more than one month after, compared to
64.2% in 2020. Although the trend has been improving
in the last year thanks to different efforts to ensure
timely access to help, the situation still points towards
the need of innovative methods to increase outreach
and information sharing of available GBV services with
refugees and local communities and the importance of
seeking timely assistance in particular for survivors of
sexual violence.

**Time between incident and disclosure**

**More than** **64.2%**
**Month** **59.9%**

**2 Weeks-**
**Month**

**6-14 Days**

**4-5 Days**

**0-3 Days**

**7.0%**

**9.0%**

**10.7%**

**11.6%**

**3.2%**

**3.6%**

**14.8%**

**15.9%**

0 10 20 30 40 50 60 70

**2020** **2021**


Input:
5

## 2 JORDAN GBV IMS Task Force Annual Report 2021
##### **Context**

Twelve years into the Syrian crisis, refugees remain in
exile as their country continues to face a protracted
conflict and an overwhelming humanitarian crisis. The
Jordanian Syrian border has remained closed for new
refugee entries into Jordan since June 2016. Borders
were temporarily closed for Syrian refugees› departure
during COVID-19 but they were starting from 8th of
August 2021. As of the 31st of December 2021, 6,254
Syrian refugees returned to their country of origin
due to high living costs in the country of asylum and
assistance reduction, lack of work opportunities or for
family reunification - especially for unaccompanied and
separated children.
As of the 31st December 2021, the United Nations
High Commissioner for Refugees (UNHCR) recorded
672,952 registered Syrian refugees in Jordan, a number
that has remained consistent over the past fouryears due to the increased entry restrictions into the
Kingdom. Among the Syrian refugee population 26%
are women, 24.6 % are men, 24.3 % are girls and
25.3% are boys. Women and girls represent more than
half of the refugee population (50.3%).
In Jordan, close to 80.5% of registered refugees live
outside the camps, primarily concentrated in urban
and rural areas in the northern governorates of Jordan,
with lesser population in the southern governorates.
The remaining Syrian refugees live in camps, mainly in
Zaatari Camp ( ± 80,708), Azraq Camp ( ± 43,936) and
the Emirati Jordanian Camp ( ± 6,667). Jordan also hosts
refugee populations from other countries. The total
number of Yemenis registered with UNHCR is 12,777.
They are to be added to the multiple other refugee
populations that Jordan hosts, including 66,362 Iraqis,
and more than 7,972 from Sudan, Somalia, and other
countries.
The continuously worsening economic situation in
Jordan due to COVID-19 was one of the motives to
return to the country of origin. According to CARE2021
Annual Needs Assessment, refugees expressed
their beliefs that reduction in assistance – caused by
underfunding humanitarian programs and the absence
of one refugee approach implementation - is a strategy
to persuade them to return to their countries of origin [3] .
According to data of the Department of Statistics in
Jordan for the third quarter of 2021, unemployment rate
has reached 23.2% (21.2% for males and 30.8% for
females), which represents a decrease in employment
rates by 1.5% for males and 2.3% for females

3 2021 Annual Needs Assessment – Care, Jordan, Page 5 https://data.
unhcr.org/en/documents/details/92107

comparing to the second quarter of the same year. The
statistics reflect a constant impact of COVID-19 on the
economic situation of Jordan and labor market with a
slight enhancement by 0.7% compared to the second
quarter of 2020.
Refugee occupations are generally limited to either
informal work in the field of agriculture, construction,
manufacturing, or incentive-based volunteering
opportunities which are mainly available in refugee
camps. Confining opportunities to these sectors and
the expensive fees of issuing work permits are the main
challenges that limited refugees from participating in
the labor market and may expose them to significant
risks of detention and exploitation. Moreover, only
Syrian refugees in Jordan are legally allowed to work.
Those from other countries, including Iraq, Yemen,
Sudan and Somalia, are not able to apply for permits [4] .
As of September 2021, the number of work permits
issued for Syrian refugees reached 258,786, only 5.6%
of them were for women [5] .
While for women, employment and income generating
opportunities continue to be governed by the
expectation of the society to fulfill their gender roles.
Increasing women participation in labor force was also
prohibited by restrictions on their movement and social
interaction under the pretext of protecting them from
sexual abuse and harassment, domestic care burdens,
and supporting children education due to schools’
closure during the pandemic.
Since the start of COVID-19, the need to meet basic
needs for families remained the first concern and
increased with the continuation of the pandemic. Many
women were compelled to get loans or debts to cover
daily expenses. Among Syrian refugee, women got
loans to pay rent, utilities and previous debts [6],while
Jordanian women got loans to pay for the acquisition
of cars, housing renovations/acquisitions, wedding
expenses, previous debts, or to pay rent and utilities for
them and their family members [7] .
According to SIGI, in 2020 there were 62,000
Jordanian women who were fined, and 23,000 were
sentenced for having debts with finance and small
project companies, as well as banks [8] .

4 https://www.unhcr.org /news/press/2022/1/61effaa54/
jordan-issues-record-number-work-permits-syrian-refugees.
html#:~:text=With%20an%20unemployment%20rate%20
of,able%20to%20apply%20for%20permits.
5 Livelihoods Dashboard_September 2021https://data.unhcr.org/en/
documents/details/89396
6 Women’s Financial Vulnerability: Challenges Women Face with Debt
and Loans https://data.unhcr.org/en/documents/details/87870
7 Women’s Financial Vulnerability: Challenges Women Face with Debt
and Loans https://data.unhcr.org/en/documents/details/87870
8 https://www.sigi-jordan.org/?p=10886

data of the Department of Statistics in Jordan


Input:
6

## **3**
##### **Main trends**

**a) Sex and age of SGBV**
**survivors**

During 2021, 94.5% of survivors assisted by data
gathering organizations were female -this is in line with
global GBV trends highlighting that women and girls are
disproportionately affected by GBV. This trend has been
consistent across the last 5-year period. Home remains
unsafe for women and girls, 86.2% of perpetrators are
intimate partners (husbands in this context), caregivers
or family members (fathers, brothers, extended family
members or in laws), while 5.4% of incidents reported
were perpetrated by a known person to the survivor,
such as family friend, neighbor, employer, service
providers or other community members. Only 7.9% of
perpetrators were unknown or have no relation to the
survivor such as taxi drivers, gas cylinders distributor,
landlords, community based organization staff, or
strangers committing sexual harassement through
social media platforms/internet, or in the streets and
public places such as markets and parks.

**Reported incidents by Age & Gender**

**Boys** **Boys**

**Men**

**0.4%** **Girls** **0.4%**

**Men**
**5.1%**

**Men**
**Girls** **0.4%** **Girls**
**5.5%**
**5.9%** **6.8%**

**2021** **2020**

**Women** **Women**
**88.6%** **87.3%**

Reported GBV incidents by girls continue to decrease
compared to the previous years despite the ongoing
efforts of GBV service providers to reach girls by
providing awareness raising sessions that target
families and tailored GBV programs for adolescent
girls. The challenge in reporting GBV incidents by
girls is related to the perpetrator-survivor relationship,
since they mostly occurred in the context of domestic
violence perpetrated by the caregiver or family
members. This is usually associated with fear of family
breakups, stigma and isolation by the community or
retaliation as well as feelings of guilt or self-blame for
being abused or when reporting violence. Moreover,

schools’ closure during COVID-19 has impacted the
attendance and dropout rates for children. According to
a study conducted by UNICEF in December 2020, outof-school rates are higher for children of non-Jordanian
nationality. More than 50,600 “Syrian refugee
children, 39,800 Jordanians, and 21,500 children of
other nationalities are estimated to be out of school.
Nationally, out-of-school rates are higher for boys than
for girls, apart from Jordanians in the 6–11 age group
where girls have a higher out-of-school rate than boys.
The number of children at risk of dropping out in the
school year 2017/18 was 40,647” [9] . Besides, attending
school personally had slightly granted girls freedom
of movement to go to school or attend safe spaces
activities which indisputably facilitated disclosure in a
safe atmosphere.
Research has found that persons with disabilities
(PWDs) are at least three times more likely to
experience physical violence, sexual violence, and
emotional violence than persons without disabilities [10] .
Women with disabilities in particular are up to 10 times
more likely to experience sexual violence, and estimates
suggest that 40 percent to 68 percent of young women
with disabilities will experience sexual violence before
the age of 18 [11] .
Collected data in 2021 indicates that only 2.7% of
incidents reported were by survivors with disabilities.
In line with the previous years’ trend, more people
with physical disability reported incidents compared to
people with mental disability.

**% of GBV survivors with disabilities**

**No disability**
**97.3%**

**Physical disability** **Mental disability** **No disability**

9 Jordan Country Report on Out-of-School Children https://www.unicef.
org/jordan/reports/jordan-country-report-out-school-children
10 Management Sciences for Health & UNFPA, We Decide Young Persons
with Disabilities: Equal Rights and a Life Free of Violence (2016),
https://www.msh.org/blog/2016/08/12/we-decide-young-personswith-disabilities-call-for-equal-rights-and-alife-free-of.
11 Id.

**Physical**
**disability**
**2.3%**

**Mental**
**disability**
**0.4%**


Input:
7

Compared to 2020, the rate hasn’t changed despite the
focused efforts to reach out to persons with disabilities.
This comes as a result of lacking equipped services
in some areas of Jordan for PWDs in addition to the
absence of affordable and accessible transportation.
Survivors with disabilities may also need to be
accompanied by a family member to reach service
providers, but since reported incidents are mainly
perpetrated by family members, it becomes challenging
to find supportive family members who would agree to
accompany the survivor to help centers. Nevertheless,
social norms impose other restrictions on PWDs and
their families in terms of acceptance and respect of
their rights which forced some families to hide their
members with disability.

**b) Types of Sexual and Gender**
**Based Violence**

The GBV IMS categorizes GBV into six broad
categories: rape; sexual assault; physical assault; forced
marriage; denial of resources/opportunities/services;
and psychological/emotional abuse.

**Sex/age and GBV type**

**100** **1.8%** **1.3%**

**66.7%**

**4.2%**

**16.7%**

**2.6%**

**10.1%**

**1.9%**

**25.9%**

**58.2%**

**12.5%** **10.8%**

**80**

**60**

**40**

**20**

**5.3%**

**8.3%**

**43.6%**

**15.7%**

**25.2%**

**0.7%**
**0.3%**

**62.5%**

**10.8%**

**14.9%**

**Girls** **Women** **Boys** **Men**

**Rape**

**Sexual Assult**

**Denial of resources,**
**opportunities of services**

**Forced Marriage**

**physical Assault**

**Psychological/Emotional**
**Abuse**

In line with previous years, the main types of GBV
reported were psychological abuse (53.9%), physical
assault (24.4%) and denial of resources, opportunities

**JORDAN GBV IMS Task Force** Annual Report 2021

or services (9.4%), mainly in the context of domestic
violence/intimate partner violence.
Psychological/emotional abuse most commonly occurs
in the form of “humiliation” and “confinement” by
intimate partners (most typically husbands). In addition,
this category also includes incidents of “verbal sexual
harassment” and online harassment with more time
spent online and on social media. Physical violence
was also mostly perpetrated by intimate partners and
took the form of beatings, slapping, and kicking among
other types of violence. It is important to underline that
physical assault has severe consequences on survivors
and may result in the death of the survivors or cause
disability. “Denial of resources” is the third most
reported type of GBV. Women and girls are increasingly
reporting incidents of denial of resources, opportunity
and services mainly perpetrated by their husbands
and male relatives. Male perpetrators prevent women
from having access to citizenship or documentation.
Women are also excluded from decision-making within
the family or around the use of cash assistance, while
others also report that their husbands would confiscate
their salaries (employers are also reported for
withholding part of the salary). Some survivors shared
that their husbands/male relatives would prevent them
from accessing reproductive health and mental health
services. In addition, women saw their inheritance rights
curtailed as well as their rights to alimony or custody.
Finally, women reported being denied opportunities
to work as well as access to women empowerment
activities or education.

Controlling behaviors reported by girls include denial
of access to school and tertiary education, limitations
of movement and social contacts as well as access
to reproductive health services for unmarried girls.
Husbands or male relatives also prevent girls from
attending girls’ empowerment activities and other
services. Denial of resources is therefore normalized
within communities, women and girls are often unaware
these incidents constitute gender-based violence.
Child marriages made up the largest number of forced
marriages, predominantly affecting girls of 15-17 years
old. Forced marriage constitutes only 4.3% of all of the
reported cases, suggesting that few girls seek help to
prevent marriage from occurring, but it is not indicative
of prevalence. More than 1 in 4 children are married
before the age of 18, and nearly 1 in 10 are married
before the age of 15-years. Recently released statistics
from the Supreme Judge Department show a slight
decrease in 2021, from 11.8% of registered marriages
in 2020 to 10.6% in 2021. This is reflecting the recent
improvement in legislation to raise the age of marriage
as well as the implementation of the National action
plan against child marriage and on the ground initiative
to reach at risk adolescent girls and their families.

statistics from the Supreme Judge Department


Input:
# Population, Poverty, and Climate Change

Monica Das Gupta This literature review focuses on the relationships between population, poverty, and climate change. Developed countries are largely responsible for global warming, but the brunt of the fallout will be borne by developing countries in forms such as lower agricultural output, poorer health, and more frequent natural disasters. Although carbon emissions per capita have leveled off in developed countries, they are projected to rise rapidly in developing countries because of economic growth and population growth.
Unfortunately, the latter will rise most notably in the poorest countries, combining with climate change to slow poverty reduction. These countries have many incentives to lower fertility. Previous studies indicate that in high fertility settings, fertility decline facilitates economic growth and poverty reduction. It also reduces the pressure on livelihoods and frees resources that can be used to cope with climate change.
Moreover, slowing population growth helps avert some of the projected global warming, which will benefit the poorest countries far more than it will benefit developed countries that lie at higher latitudes and/or have more resources to cope with climate change.
Natural experiments indicate that family-planning programs are effective and highly pro-poor in their impact. While the rest of the world wrestles with the complexities of reducing emissions, the poorest countries will benefit from simple programs to lower fertility. Population, Poverty, Economic growth, Climate change, Global warming, Family planning, sub-Saharan Africa, Developed countries, Developing countries, Public policy, Sustainable development, Ecological injustice JEL codes: Q56, Q54, J13, J18 The relationships among population dynamics, poverty, and climate change are now recognized in the literature on sustainable development. This paper summarizes the evidence currently available on these relationships and their implications for the poorest developing countries. The paper begins with a review of the The World Bank Research Observer
# The Author 2014. Published by Oxford University Press on behalf of the International Bank for Reconstruction and
Development / THE WORLD BANK. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com doi:10.1093/wbro/lkt009 Advance Access publication January 6, 2014 29:83–108


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Moreover, “property rights to natural capital are frequently unprotected or ill-specified. . ., (which) typically leads to their overexploitation, and so to waste and inequity” (Dasgupta 2010, 6).

Arrow et al. (2004, Table 2) estimate how much “genuine wealth per capita” (including natural capital, human capital, and manufactured capital) changed during 1970–2000. The estimates are necessarily approximate, but they have been made carefully, and the results are instructive. They find that although GDP per capita grew quite rapidly during 1970–2000 in all regions except sub-Saharan Africa, rates of growth in “genuine wealth per capita” were far lower. They declined sharply in sub-Saharan Africa and in the Middle East and North Africa (by -2.6% and -3.8% per year, respectively), and they grew very slowly (well below 1% per year) in South Asia and the United States. They grew rapidly only in China because of its low population growth and heavy investment in productivity. Revising the method to include more information on growth in human capital and institutional change, Dasgupta (2010, 9–10) derives far lower estimates of growth in genuine wealth per capita for China 1970–2000, and for South Asia, he estimates a decline of between -0.4% per year (India) and -1.4% (Pakistan).
Human ingenuity has faced an uphill task in devising ways of managing common property resources given the institutional and political challenges in aligning divergent interests. Markets are very poor at incentivizing people not to overuse resources that are unpriced or under-priced relative to social cost (Arrow 1969; Dasgupta 2001; Stern 2006), especially in the case of transnational common resources (Dasgupta et al. 1997). The consequent negative externalities need to be addressed through collective action, but in the absence of strong mechanisms for mutual coercion and cooperation, it is very difficult to align the interests of different stakeholders to this end. Ostrom (1990) has argued that common property can be successfully managed by user associations in small communities if eight “design principles” are met, including the ability to effectively exclude unentitled parties. Such conditions clearly do not apply to global common resources. As Lee (1990, 317) notes, “Each birth inflicts costs on all others by reducing the value of their environmental birthright”. [2] The juxtaposition of these scientific, executive, and political challenges places high demands on the ability of national and global institutions to respond to these challenges, as evidenced by the slow progress made in decades of efforts to regulate carbon emissions. The original projections of the Limits to Growth study for the 1970–2000 period correspond broadly with the observed trends during this period (Turner 2008, 2012).

Managing Climate Change: Addressing Per-Capita Emissions and Population Models of climate change take population size into account, but they typically treat it as a given (for example, Stern 2006; Nordhaus 2008, 2012). They tend to use Das Gupta 87


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the United Nations medium variant population projections. Using this approach, the World Bank (2010, Figure 3.5) estimates the impact of climate change on the growth in agricultural productivity required to meet the world’s rising food demand. The model incorporates projected rises in food demand due to growth in incomes as well as in population size and shows how much more difficult it will be to meet that demand given anticipated climate change. What is needed is a huge increase in agricultural productivity backed by greatly intensified regulation to protect natural systems.
However, as we discuss below, population size is amenable to policy, and it makes a significant difference to the size of adjustments required on other fronts. Models vary, but the World Bank (2010) estimates that to meet the growing demand for food between 2005 and 2055, agricultural productivity will need to rise by 64% under the assumptions of the “business-as-usual” scenario and by a further 80% to offset the projected stresses arising from climate change (Figure 1). However, the model indicates that if population remained constant at the 2005 level, agricultural productivity would need to rise only 25% under the “business-as-usual” scenario; that is, more of the required productivity increase under the “business-as-usual” scenario is necessitated by population growth than by increases in consumption per capita.
The developed countries’ carbon emissions per capita are far higher than those of the developing countries, but the latter account for nearly all of the projected increase in emissions between now and 2050 (Stern 2006, Figure 7.3). Although emission rates in the developed countries seem to have peaked, they are growing rapidly in the developing countries due to both economic growth and population growth. Although China has had the steepest growth in carbon emissions with its high rate of economic growth, its estimated total emissions in 2008 were similar to those of other developing countries as a group (excluding India), [3] partly because the latter had twice the population of China (UN 2013). GDP per capita is rising rapidly across developing countries, including sub-Saharan Africa in the 2000s (IMF 2010, 2011). Nearly all of the projected global population growth will occur in developing countries, whose population (excluding China and India) is projected to grow 2.7-fold between 2000 and 2100, driven largely by the six-fold increase projected for sub-Saharan Africa (UN 2013, medium variant). It is estimated that the effect of a 40% reduction in CO2 emissions per capita in developed countries between 2000 and 2050 would be entirely offset by the increase in emissions attributable to expected population growth in poorer countries over this period, even if we assume no change in emissions per capita in these countries (Dyson 2005). [4] Managing global warming may require different policy approaches in different settings. Imposing a carbon tax is strongly recommended as the simplest way to reduce carbon emissions (Stern 2006; Nordhaus 2008, 2012). By putting a price on carbon emissions, such a tax creates incentives to conserve global common 88 The World Bank Research Observer, vol. 29, no. 1 (February 2014)

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the currently developed countries did not have to face when they were industrializing rapidly.
For countries that still have high levels of fertility, which typically are still poor and have very low per capita emission rates, the key approach seems to be to reduce fertility. Clearly, they do not owe it to the world to reduce fertility to help slow the pace of climate change. However, they have strong incentives of their own to reduce fertility, as discussed below.

## The Unequal Impact of Climate Change

The developed countries are responsible for much of the accumulation of emissions that affect climate change because they began rapid industrialization by the end of the nineteenth century. However, in a twist of fate, the impact of climate change will be felt most sharply in the developing countries,. Many of these countries lack the financial resources for adaptation/mitigation efforts, and for some, the capacity to act may also be hindered by poor governance (WDR 2008, 245).

“The impacts of climate change are not evenly distributed - the poorest countries and people will suffer earliest and most. . ..First, developing regions are at a geographic disadvantage: they are already warmer, on average, than developed regions, and they also suffer from high rainfall variability. . ..Second, developing countries - in particular the poorest - are heavily dependent on agriculture, the most climate-sensitive of all economic sectors, and suffer from inadequate health provision and low-quality public services. Third, their low incomes and vulnerabilities make adaptation to climate change particularly difficult. . ..At a national level, climate change will cut revenues and raise spending needs, worsening public finances.” (Stern 2006, vii) Modeling the effects of climate change is subject to considerable uncertainty, but there is consensus on its broad effects, some of which are summarized below.

(a) Food and Water: Global warming will reduce crop outputs at lower latitudes, undermining food security in the developing countries. If temperatures rise further, global food output will decrease, [5] reducing the developing world’s access to imported food.
Approximately one-third of the world’s population lives in countries with moderate to high water stress (Stern 2006, 63), often exacerbated by poor management of water resources (World Bank 2007, 183). Rising demand for agriculture and other purposes will heighten water scarcity.
Climate change is also expected to disrupt rainfall patterns, threatening agricultural cycles and human lives with droughts and floods. These changes will 90 The World Bank Research Observer, vol. 29, no. 1 (February 2014)


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Figure 4. Population Projections for Sub-Saharan Africa Indicate that Maintaining One Less Birth per Woman Halves the Estimated Population in 2100 Source: United Nations (2013).

- The UN “high” and “low” projections diverge during 2010-2020, reaching a difference of 1 birth by 2020.

especially important for the poor, who typically have higher numbers of unwanted children than the rich, except in settings with very effective programs, such as Indonesia (Figure 5).
Family-planning programs also typically seek to reduce desired family size by disseminating information on new opportunities for altering living standards through new strategies for bearing and investing in children. Parents - especially poorer parents - have imperfect information on these issues. Households also appear to face difficulties in making optimal choices that involve long-term planning horizons (see, for example, Cronqvist and Thaler 2004 on pension decisions). People’s access to information can be improved by offering simple messages through mass communication or more complex messages through radio or television soap operas that portray the lives of people with small families and how they access new opportunities helps reduce imperfect information.
Media outreach has been found to be effective at increasing contraceptive use and lowering fertility. This has been found in many studies using cross-sectional survey data on access to media (e.g., Bhat 1998). The few quasi-randomized evaluations of media outreach have found it effective at altering fertility and contraceptive 96 The World Bank Research Observer, vol. 29, no. 1 (February 2014)

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especially auspicious to bear children. They found that larger cohorts of children are born in auspicious years and that these children have higher schooling attainment. They concluded that this is because parents are more likely to invest in planned children. Analyses of natural experiments in the United States and Sweden indicate that increasing women’s ability to plan their births was associated with substantial improvements in their children’s education and earning capacity (Rotz 2013, Madestam and Simeonova 2013).

Are Family-Planning Programs Likely to Work in the Poorest Countries?

The experience of countries such as the Republic of Korea in the 1960s and others such as Indonesia, Bangladesh, or Nepal shows that sustained fertility decline can occur in poor countries given political commitment to family-planning programs.
This commitment is driven by poverty and sharply rising population growth rates.
For example, India’s censuses showed decadal growth of 11%-14% from the 1920s, but this jumped to 22% in 1951–61 and 25% during 1961–71. Similar population growth rates were observed in the Asian region and sub-Saharan Africa (UN 2013). However, sub-Saharan Africa’s small population base in the 1950s meant low increases in numbers, a situation that is changing very quickly (Figure 4).
Political commitment to family planning has sometimes been low in many subSaharan African countries, which may have contributed to their slow fertility decline (Cleland et al. 2006, 2011; Bongaarts 2006; Machiyama 2010). [11] However, this situation can change quickly, as evidenced by the success of the Rwandan government’s concerted push since the mid-2000s to reduce fertility.
Until then, both Rwanda and its neighbor, Burundi, were poor, densely populated countries with high fertility and weak family-planning programs. Then, in contrast with Burundi, Rwandan government officials spoke out about the need to reduce fertility. A country-wide information dissemination program was implemented, along with sharply increased access to contraceptive methods. Between the DHS surveys of 2005 and 2010, the total fertility rate fell from 6.1 to 4.6 children per woman, and the use of modern methods of contraception among married women rose from 10% to 45% (National Institute of Statistics of Rwanda 2011).
Meanwhile, total fertility in Burundi was 6.4 in 2010 (Institut de Statistiques et d’E [ ́] tudes E [ ́] conomiques du Burundi 2010).
Most countries in sub-Saharan Africa show some fertility decline, indicating a desire to lower fertility. Family-planning programs can build on this desire and accelerate fertility decline. These programs are likely to be most effective when accompanied by other measures addressing basic government failures that help sustain poverty and high fertility, including efforts to improve health and schooling and to expand income-earning opportunities. Family-planning programs help by increasing access to contraception and by providing informational outreach to accelerate 100 The World Bank Research Observer, vol. 29, no. 1 (February 2014)


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per-capita resources to strengthen systems for disaster management and for disease prevention and control, helping them to cope more effectively with the multiplicity of stresses associated with climate change. It will reduce growth in the demand for jobs and livelihoods. Moreover, studies from both developing and developed countries show that access to birth planning helps reduce poverty through increased female labor force participation and better child schooling and health outcomes.
Furthermore, fertility decline in poor countries yields a substantial “demographic dividend” in reducing poverty and vulnerability, even without the large additional gains that can be obtained with strong economic policies.
Lower fertility will also benefit the poorest countries by reducing the pace of future global warming, the negative effects of which affect them far more than developed countries. The latter countries mostly lie at geographically higher latitudes that are less negatively affected by climate change. Furthermore, the developed countries have far greater resources to cope with climate change.
The means of lowering fertility are well documented. Family-planning programs help by increasing access to contraception and by catalyzing demand for contraception through media blitzes. Studies show that family-planning programs are effective at helping to lower fertility, and highly pro-poor in their impact. Easier access to family-planning most benefits women who are poor and uneducated. Family-planning programs are a simple, effective, and relatively inexpensive way to achieve a multiplicity of benefits for poor countries.
While the rest of the world wrestles with the political and technological complexities of reducing emissions, family-planning programs offer the poorest countries a simple and effective means to reduce poverty and mitigate the impact of climate change.

## Notes

Research Professor, Department of Sociology, University of Maryland, College Park, MD and Visiting
[Fellow, Population Reference Bureau, Washington, DC. Email: mdasgupta@gmail.com. This work was](mailto:mdasgupta@gmail.com) supported by the William and Flora Hewlett Foundation [Trust Fund TF070424 to the World Bank and grant number 2012-7611 to the Population Reference Bureau]. Comments from Emmanuel Jimenez and an anonymous reviewer were very helpful for revising the draft, all errors remain mine.

1. Simon and Boserup both argued that higher population densities can increase the economies of
scale in providing productivity-enhancing infrastructure and services, such as transport and extension services (Glover and Simon 1975; Boserup 1981).
2. See also Lee and Miller (1990).
3. United States Environmental Protection Agency (nd) Global Greenhouse Gas Emissions Data
[http://www.epa.gov/climatechange/ghgemissions/global.html (accessed 5 June 2013).](http://www.epa.gov/climatechange/ghgemissions/global.html) 4. Many have estimated that slowing population growth could substantially reduce carbon emissions (see, for example, Meadows et al. 1972; Bongaarts 1992; and O’Neill et al. 2010).
5. World Bank 2007, 200; FAO 2009, 29; Stern 2006, 67; Potsdam Institute 2012.

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**1.** **Introduction**

Agricultural risk management is an inherent part of the development and poverty agenda because it can contribute significantly to poverty reduction. In most countries, and particularly in developing countries, farmers’ income is highly vulnerable to numerous risks, especially the production risk commonly associated with weather related events. Such events cause significant income fluctuations, can be a major threat to farmers’ financial survival, and can be difficult to manage. The inherent instability of farm income has led governments to devise numerous agricultural income stabilization programs and policies, with crop insurance being among the most prevalent. A survey conducted by the Food and Agriculture Organization (FAO) in the early 1990s reveals that various types of crop insurance programs are present in more than 140 countries (FAO 1991). Most of them, if not all, rely heavily on government subsidies.

However, despite the prevalence of government subsidies, the overall financial experience of traditional crop insurance has been disappointing and, in many instances, disastrous. Low insurance penetration despite high premium subsidies, mostly captured by large farmers, consistent underestimation of the systemic component of production risk, poor financial performance with claims consistently exceeding premiums, inappropriate pricing methodologies, uncontrolled moral hazard, and adverse selection are a few of the key endemic problems that plague national agricultural insurance programs worldwide (Hazell, Pomarelda and Valdes 1986; FAO 1991; Anderson 2001) [1] .

Previous government efforts to develop viable national crop insurance programs have also been frustrated by the inherent lack of clarity regarding the objectives that, depending on policymakers’ view, range from providing social safety nets to risk management. As a risk management instrument, crop insurance, in addition to other financial instruments (savings and credit) and preventive activities, can play a vital role in enabling farmers to deal with production risk and help them to restore their business viability after crop failures. As a social instrument, crop insurance can protect poor farmers from the economic consequences of adverse production shocks, thus restoring their livelihoods.

In the early 1980s, the realization of these daunting challenges led the World Bank to withdraw from providing technical and financial support to government sponsored agricultural insurance programs. However, since the late 1990s, due to the dwindling government subsidies to agricultural producers in developing countries, there has been a renewed interest among development lenders in using agricultural insurance as an important risk management tool that can enhance the business viability of agriculture in developing countries. The emergence of a new generation of parametric and index-based insurance products, which to a large extent, manage to address the moral hazard problem, was another contributing factor that led to revisiting the potential role of agricultural insurance in these economies. The World Bank has initiated a series of feasibility studies on weather-based index insurance in several countries (including Morocco, Nicaragua, and Tunisia) but, until now, none of these innovative programs has been implemented on a larger scale due to the lack of a coherent integrated and practical agricultural risk management framework that could ground these types of instruments in the concept of _crops’ market viability_ - the main topic of this work.

1 Hazell, Pomarelda and Valdes (1986) provide a comprehensive analysis of crop yield risk management in developing countries. They investigate the role of public agricultural risk management policies in general and crop insurance in particular in dealing with the variability of farm income.

1

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_positive_ . [6] Under this approach, the crop business profitability is determined by the downside risk characteristics of the specified crop. If the crop yield is lower than the production costs, the survivability of the crop business activity will depend on the farmer’s ability to absorb this income shock. The viability of the crop business thus relies on the farmer’s financial reserves or external capital to cover the unexpected operating losses, which is the minimum amount of capital required to ensure the continuity of the business after the occurrence of an adverse production shock. The relationship between the crop yield risk characteristics and the minimum capital requirements is illustrated in Box 1.

**Box 1. Crop yield losses and minimum capital requirements**

Data on cocoa and banana yields from 1961 to 2002 in a South American country are used for the sake of illustration. Data are first detrended using a polynomial procedure. First order serial correlation is tested and the hypothesis of serial correlation is rejected. The figure below shows the variability of these two crops expressed in percentage of the average (adjusted) crop yields, which are estimated at 168.89 kg/ha for cocoa and 23.42 mt/ha for banana. Cocoa yields are more variable than banana yields and exposed to higher extremes.

**Variability of banana and cocoa, in percentage of average yields.**

200.00% 180.00% 160.00% 140.00% 120.00% 100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

**year**

banana cocoa The cumulative distribution functions (CDF) of the operating losses are simulated for different levels of average operating profit margin. The downside risk is measured by the Value at risk (VaR) which summarizes the worst loss over a target horizon with a given level of confidence. Formally, this is a specified quantile of the CDF of the operating losses. For example, VaR(5%) is the worst operating loss in a 95% confidence interval. It determines the minimum capital required to absorb potential losses and thus ensures the viability of the crop business following the occurrence of a loss. The higher the average operating profit margin is, the lower are the potential losses and thus the minimum business capital requirements.

The table below presents the minimum capital requirements for two different crops depending on the farmer’s average operating profit margin. It shows that the reserves required for cocoa production are much larger that those required for banana production, as a consequence of the greater (downside) variability of the cocoa yields.
When the average operating profit margin is 5%, the amount of capital needed to absorb unexpected production shocks with a 95% confidence interval is equal to 58.2% of the average yield for cocoa and 19.7% for bananas.
When the average operating profit margin is 20%, the amount of capital is 46.2% and 2.9% for cocoa production and banana production, respectively.

6 Formally, operating loss= max[1( −α) ~~_y_~~ - _y_ 0,], where ~~_y_~~ is the average crop yield, _y_ is the actual yield and α is the average operating profit margin. Operating loss is thus equal to negative operating profit margin.

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**Minimum capital requirements (in % of average yields)**

Average operating profit margin (in % of average yields) 5% 10% 15% 20% 25% Cocoa 58.2% 54.7% 49.4% 46.2% 41.4% Banana 19.7% 13.1% 4.3% 2.9% 1.4% Source: Authors.

The amount of capital required to ensure the viability of the business in the long run may be high and it increases with the exposure of the crop yield to extreme events. The concept of _catastrophic risk adjusted (CRA) crop yield_ is introduced to examine the impact of catastrophic events on the crop business profitability. It is defined as a crop yield adjusted for catastrophic events where, following an actuarial approach, catastrophes are defined as infrequent events, e.g., occurring once in twenty years or more. Therefore, the downside risk caused by catastrophic events is removed with an hypothetical put option priced at its fair value. The minimum capital requirements are estimated from the operating losses adjusted for the catastrophic risk. Obviously, they are lower than those non-adjusted for catastrophic risk because the catastrophic events have been removed. The value added created by the catastrophic coverage increases with the degree of exposure of the crop to extreme events.
This is illustrated in Box 2.

**Box 2. Catastrophic risk adjusted crop yields and minimum capital requirements**

Detrended data on cocoa yields and banana yields from 1961 to 2002 in a South American country are used for the purposes of illustration. A catastrophic event is defined here as an event occurring on average once in 20 years or less frequently. Given the simulated cumulative distribution function of the crop yields, this catastrophic event occurs when the national cocoa yield falls below 70 kg/ha (strike). This event has occurred twice since 1961 (1983 and 1987). The actuarially fair price of this hypothetical coverage (i.e., put option with a strike at 70 kg/ha) is estimated at 0.72% of the average yield.

**Crop yields and catastrophic risk adjusted crop yields (one in 20 year event), national cocoa yield**

350 300 250 200 150 100 50 0 yield CRA yield trigger year 7

Detrended data on cocoa yields and banana yields


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**Figure 2b.** **Ineffective risk financing instruments**

Expected return B Efficient frontier without risk financing instruments Efficient frontier with risk financing instruments Return variability Hedging costs Source: Authors.

A C

**Box 3. Risk financing instruments as market-enhancing solutions**

Detrended data on cocoa yields from 1961 to 2002 in a South American country are used for the sake of illustration. The hedging costs represent the costs of self-retaining risks and/or the insurance costs, beyond the fair premium.

The catastrophe coverage is assumed to be sold at twice its fair price. The catastrophe insurance costs are thus equal to 0.72% of the average yield for the production of cocoa and 0.29% for banana. The non-catastrophe coverage faces insurance costs equal to 50% of the fair premium (i.e., 50% loading factor), for the sake of simplicity.

If the crop yield risk is self-retained, the costs of holding the minimum capital requirements (reserve load) represent 15% of the minimum capital (see Box 2).

The cost of self-retention (reserve load) is higher than the cost of non-catastrophe insurance in the case of the production of cocoa, whatever the average operating profit margin. The costs of hedging against cocoa yield risks are thus equal to the sum of the catastrophe insurance cost and the non-catastrophe insurance costs. On the contrary, self-retaining yield risk is less expensive than transferring the non-catastrophic component of this risk in the case of banana production. The costs of hedging against banana yield risks are thus equal to the sum of the catastrophe insurance cost and the reserve load.

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**Figure 4. Testing the viability of the crop business**

Production costs Crop yields Operating profit margin Operating losses Minimum capital requirements Self-insurance Non-catastrophe Catastrophe insurance insurance Reserve load Non-catastrophe Catastrophe insurance costs insurance costs Hedging costs (Non)-Viability of the crop business Source: Authors.

**Box 4. Crop business viability**

Detrended data on cocoa yields from 1961 to 2002 in a South American country are used for the sake of illustration.

Without access to external capital, cocoa production would be a viable business only for large asset-base farmers given the high level of minimum capital requirements (see Box 2). On the contrary, banana production is less variable and thus the minimum capital requirements are lower, allowing asset poor farmers to be involved in a viable business (see Box 2).

Access to external capital (through insurance) may enhance or restore the viability of the crop business. The cost of hedging against catastrophic losses and hedging/self-retaining non-catastrophic losses is assessed from the 13