Quantifying SDG indicators for multiple SSPs up to 2050 with a focus on selected low and low-middle income countries and the bio-economy based on CGE analysis

A wide range of indicators beyond GDP growth is necessary to measure progress towards more sustainability as reflected by the indicator frameworks developed by the United Nations (2021). Still, such progress builds on its core on economic growth and related structural change. Given its multi-sector and global perspective, dynamic CGE analysis depicts these key processes and thus offers a starting point to quantify various SDG indicators. Multiple scholars have therefore developed SDG indicator frameworks which fit their CGE models, such as Philippidis et al. (2020) and Lui et al. (2021). Existing auxiliary data available from GTAP, such as CO2 (Peters, 2016), non-CO2 (Chepeliev, 2020a) and air emissions (Chepeliev, 2020b) already help to access important aspects of environmental sustainability and to relate emissions to human health. Further indicators require partly sector and product detail beyond the GTAP Data Base which motivates the development of more detail data base in this study. Distributional aspects of economic growth, also beyond income distribution, remain a challenge in CGE analysis, and are addressed in this study by micro-simulations. We propose to quantify 75 indicators relating to 13 of the 17 SDGs in order to assess SDG developments up to 2050 for different Socio-Economic Pathways to extend existing work in this field

Quantifying SDG indicators for multiple SSPs up to 2050 with a focus on selected low and low-middle income countries and the bio-economy based on CGE analysis

A wide range of indicators beyond GDP growth is necessary to measure progress towards more sustainability as reflected by the indicator frameworks developed by the United Nations (2021). Still, such progress builds on its core on economic growth and related structural change. Given its multi-sector and global perspective, dynamic CGE analysis depicts these key processes and thus offers a starting point to quantify various SDG indicators. Multiple scholars have therefore developed SDG indicator frameworks which fit their CGE models, such as Philippidis et al. (2020) and Lui et al. (2021). Existing auxiliary data available from GTAP, such as CO2 (Peters, 2016), non-CO2 (Chepeliev, 2020a) and air emissions (Chepeliev, 2020b) already help to access important aspects of environmental sustainability and to relate emissions to human health. Further indicators require partly sector and product detail beyond the GTAP Data Base which motivates the development of more detail data base in this study. Distributional aspects of economic growth, also beyond income distribution, remain a challenge in CGE analysis, and are addressed in this study by micro-simulations. We propose to quantify 75 indicators relating to 13 of the 17 SDGs in order to assess SDG developments up to 2050 for different Socio-Economic Pathways to extend existing work in this field

Who is most vulnerable to climate change induced yield changes? A dynamic long run household analysis in lower income countries

Climate change impacts on agricultural production will shape the challenges of reaching food security and reducing poverty across households in the future. Existing literature lacks analysis of these impacts on different household groups under consideration of changing socio-economic developments. Here, we analyze how crop yield shifts induced by climate change will affect different household types in three low- and lower-middle-income countries, namely Vietnam, Ethiopia and Bolivia. The long-run analysis is based on a recursive-dynamic Computable General Equilibrium model. We first construct a baseline scenario projecting global socio-economic developments up to 2050. From there, we implement business-as-usual climate change shocks on crop yields. In the baseline, all households benefit from welfare increases over time. Adding climate change induced yield changes reveals impacts different in size and direction depending on the level of the households’ income and on the share of income generated in agriculture. We find that the composition of the factor income and the land ownership are of large importance for the vulnerability of households to climate change, since the loss for non-agricultural households is highest in absolute terms. The complementary comparative static analysis shows smaller absolute and relative effects for most households as the differentiated factor income growth over time is not considered, which makes household types more or less vulnerable. A sensitivity analysis varying the severity of climate change impacts on yields confirms that more negative yield shifts exacerbate the situation (especially) of the most vulnerable households. Furthermore, it underlines that yield shocks on staple crops are of major importance for the welfare effect. Our findings reveal the need for differentiated interventions to mitigate consequences especially for the most vulnerable households

Who is Most Vulnerable to Climate Change Induced Yield Changes? A Dynamic Long Run Household Analysis in Lower Income Countries

Climate change impacts on agricultural production will shape the challenges of reaching food security and reducing poverty across households in the future. Existing literature lacks analysis of these impacts on different household groups under consideration of changing socio-economic developments. Here, we analyze how crop yield shifts induced by climate change will affect different household types in three low and low middle-income countries, namely Vietnam, Ethiopia and Bolivia. The long-run analysis is based on a recursive-dynamic Computable General Equilibrium model. We first construct a baseline scenario projecting global socio-economic developments up to 2050. From there, we implement business-as-usual climate change shocks on crop yields. In the baseline, all households benefit from welfare increases over time. Adding climate change induced yield changes reveals impacts different in size and direction depending on the level of the households’ income and on the share of income generated in agriculture. We find that the composition of the factor income is of large importance for the vulnerability of households to climate change, as, the loss for non-agricultural households is highest in absolute terms. The complementary comparative static analysis shows smaller absolute and relative effects for most households as the differentiated factor income growth over time is not considered, which makes household types more or less vulnerable. A sensitivity analysis varying the severity of climate change impacts on yields confirms that more negative yield shifts exacerbate the situation of the most vulnerable households. Furthermore, it underlines that yield shocks on staple crops are of major importance for the welfare effect. Our findings reveal the need for differentiated interventions to mitigate consequences especially for the most vulnerable households