Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

Abstract

This paper proposes a centralized data envelopment analysis (DEA) model for industrial optimization based on several different production technologies among several regions. We developed this model based on improved Kuosmanen environmental DEA technology, which avoids positive shadow price on undesirable outputs. We also designed a dual model for our centralized DEA model, and used it to analyze shadow prices on CO 2 emissions. We further employed the proposed model to determine the optimal path for controlling CO 2 emissions at the sector level for each province in China. At sectoral level, manufacturing showed the highest potential emissions reduction, and transportation was the largest accepter of emission quotas. At regional level, western and northeastern areas faced the largest adjustments in allowable emissions, while central and eastern areas required the least amount of adjustment. Because our model represents increase or decrease in emissions bidirectionally in terms of shadow price analysis, this setting makes the shadow price on CO 2 emissions lower than strong regulation (decreasing CO 2 emissions along with increasing value added) used by directional distance function (DDF).

Toward a Sustainable Low-Carbon China: A Review of the Special Issue of “Energy Economics and Management”

“…Most papers in this Special Issue show great interest in the CO 2 emissions [1,7,9,11,12] and the carbon intensity [8,13]. Some of these studies use the conventional regression to analyze CO 2 emissions [1,8,13], but the downside of this method is that conventional regressions cannot solve the problems of multiple inputs/outputs in these models.…”

“…Some of these studies use the conventional regression to analyze CO 2 emissions [1,8,13], but the downside of this method is that conventional regressions cannot solve the problems of multiple inputs/outputs in these models. Therefore, a centralized DEA approach was used to determine the optimal path for controlling CO 2 emissions at the sector level for each province in China, which can avoid positive shadow prices on undesirable outputs [9]. The global directional function (DDF) and non-radial Luenberger productivity index were used to analyze the dynamic change in the total-factor carbon emission performance of industrial land use (TCPIL) [11].…”

“…Therefore, Sun et al [9] developed the traditional data envelopment analysis (DEA) model based on improved Kuosmanen environmental DEA technology, which avoids the effect of positive shadow prices on undesirable outputs and designs a dual model to analyze shadow prices on CO 2 emissions for each province and its industrial sectors in China [9]. Likewise, by adopting an energy-input based directional function, Lai et al [10] have calculated the shadow price of energy (i.e., coal, oil, gas, and electricity) across 30 areas in China.…”

“…In order to control the country's excessive energy consumption and resultant CO 2 emission problem, China has committed to reducing its CO 2 emissions by 40%-45% from 2005 levels per unit of GDP by 2020 [9]. Therefore, Sun et al [9] developed the traditional data envelopment analysis (DEA) model based on improved Kuosmanen environmental DEA technology, which avoids the effect of positive shadow prices on undesirable outputs and designs a dual model to analyze shadow prices on CO 2 emissions for each province and its industrial sectors in China [9].…”

Driving Sustainable Competitive Advantage in the Mobile Industry: Evidence from U.S. Wireless Carriers

“…DEA was developed by Charnes, et al [30] and has been applied to various areas [31][32][33][34][35][36][37]. The Charnes, Cooper and Rhodes (CCR) model and Banker, Charnes, and Cooper (BCC) model are the representative models of DEA, and depending on the input-output relationship, one can use either the CCR model, based on constant returns to scale (CRS), or the BCC model, based on variable returns to scale (VRS).…”