Carbon emissions abatement(CEA)is an important issue that draws attention from both academicians and policymakers.Data envelopment analysis(DEA)has been a popular tool to allocate the CEA,and most previous works are b...Carbon emissions abatement(CEA)is an important issue that draws attention from both academicians and policymakers.Data envelopment analysis(DEA)has been a popular tool to allocate the CEA,and most previous works are based on radial DEA models.However,as shown in our paper,these models may give biased results due to their ignorance of slackness.To avoid such problems,we propose an allocation model based on the slack-based model and multiple-objective nonlinear programming to find the CEA allocation plan,which can minimize the GDP loss.The property of nonconvexity makes the model difficult to solve.Thus,we construct an approximation algorithm to solve this model with guaranteed error bounds and complexity.In the empirical application,we take regions of china as an illustrative example and find there is a significant region gap in China.Hence,we group the regions into eastern,central,and western,and give the main results,as well as the superiority of our allocation models compared with radial models.展开更多
基金Key Laboratory of Management,Decision and Information Systems,Chinese Academy of Sciences.
文摘Carbon emissions abatement(CEA)is an important issue that draws attention from both academicians and policymakers.Data envelopment analysis(DEA)has been a popular tool to allocate the CEA,and most previous works are based on radial DEA models.However,as shown in our paper,these models may give biased results due to their ignorance of slackness.To avoid such problems,we propose an allocation model based on the slack-based model and multiple-objective nonlinear programming to find the CEA allocation plan,which can minimize the GDP loss.The property of nonconvexity makes the model difficult to solve.Thus,we construct an approximation algorithm to solve this model with guaranteed error bounds and complexity.In the empirical application,we take regions of china as an illustrative example and find there is a significant region gap in China.Hence,we group the regions into eastern,central,and western,and give the main results,as well as the superiority of our allocation models compared with radial models.
