Simulating the spatial distribution of population and emissions to 2100

Abstract

The MIT Joint Program on the Science and Policy of Global Change is an organization for research, independent policy analysis, and public education in global environmental change. It seeks to provide leadership in understanding scientific, economic, and ecological aspects of this difficult issue, and combining them into policy assessments that serve the needs of ongoing national and international discussions. To this end, the Program brings together an interdisciplinary group from two established research centers at MIT: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers bridge many key areas of the needed intellectual work, and additional essential areas are covered by other MIT departments, by collaboration with the Ecosystems Center of the Marine Biology Laboratory (MBL) at Woods Hole, and by short-and long-term visitors to the Program. The Program involves sponsorship and active participation by industry, government, and non-profit organizations.To inform processes of policy development and implementation, climate change research needs to focus on improving the prediction of those variables that are most relevant to economic, social, and environmental effects. In turn, the greenhouse gas and atmospheric aerosol assumptions underlying climate analysis need to be related to the economic, technological, and political forces that drive emissions, and to the results of international agreements and mitigation. Further, assessments of possible societal and ecosystem impacts, and analysis of mitigation strategies, need to be based on realistic evaluation of the uncertainties of climate science.This report is one of a series intended to communicate research results and improve public understanding of climate issues, thereby contributing to informed debate about the climate issue, the uncertainties, and the economic and social implications of policy alternatives. Titles in the Report Series to date are listed on the inside back cover.

Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling

“…While the SSPs are devised in terms of country scenarios, finer scenarios (e.g., scenarios in terms of 0.5-degree grids) are required to analyse regional/city-level sustainability and resiliency. A number of studies downscale country-level socioeconomic scenarios into finer spatial units (e.g., Bengtsson et al, 2006;Asadoorian, 2007;Grübler et al, 2007;van Vuuren et al, 2007;Gaffin et al 2011;Hachadoorian et al, 2011;Jones and O'Neill, 2013;Nam and Reilly, 2013;McKee et al, 2015;Yamagata et al, 2015;Jones and O'Neill, 2016).…”

Modelling Land Use, Land-Use Change, and Forestry in Climate Change: A Review of Major Approaches

“…As a result, most of the existing models, especially economically oriented, do not present spatial illustration of land disaggregation (e.g., BLS, FARM, D-FARM, GTAP). First attempts in this direction were made by Nordhaus (2006), Asadoorian (2005) and Grübler et al, (2007). However, the spatial down-scaling techniques were not so refined.…”

Inequality of Carbon Intensity: Empirical Analysis of China 2000–2014

“…Dagum's Gini coefficient and its subgroup decomposition technique can decompose regional differences into intra-regional differences, inter-regional net differences, and an intensity of transvariation intensity. Therefore, Dagum's Gini coefficient method has been used to study the inequality of energy and carbon emission issues [16][17][18][19][20][21]. In this study, we utilized the Gini coefficient and its subgroup decomposition method to investigate disparities of carbon intensity in China between 2000 and 2014, based on available and updated data.…”