Different effects of technological progress on China's carbon emissions based on sustainable development

Abstract

In this paper, we respectively decompose and study different effects of technological progress on carbon emissions in China based on the combination of the logarithmic mean Divisia index method, the Solow residual model, and spatial econometrics. Furthermore, we propose an improved approach to estimate the rebound effect index. By comparing the different effects of technological progress on carbon emissions, our results indicate that China's overall domestic technological progress reduced its carbon emissions over this period. As for the rebound effect index, the estimated results are higher than in previous studies because of the spatial rebound effect, which was ignored by previous studies. Regionally, although the eastern region had high rebound effects, the western region is at the greatest risk from the rebound effects. Finally, we present specific environmental policy proposals for China's sustainable development based on empirical results. KEYWORDS carbon emissions;logarithmic mean Divisia index, rebound effect, spatial econometrics, sustainable development, technological progress

County-level CO2 emissions and sequestration in China during 1997–2017

“…First, the estimations of provincial CO 2 emissions were carried out based on the following method provided by the Intergovernmental Panel on Climate Change (IPCC), which has been widely adopted 4 , 48 , 49 : where represents the provincial CO 2 emissions from energy use (unit: million tons); represents the j th type of energy use in province i ; is the low calorific value of the j th energy consumption; is the carbon content of the j th energy source; and is the carbon oxidation factor of the j th energy source. In addition, 17 types of fossil fuel used are considered, including raw coal, cleaned coal, other washed coal, briquettes, gangue, coke, coke oven gas, blast furnace gas, converter gas, other gases, other coking products, crude oil, gasoline, kerosene, diesel oil, fuel oil, naphtha, lubricants, paraffin, white spirit, bitumen asphalt, petroleum coke, other petroleum products, liquefied petroleum gas (LPG), refinery gas, and natural gas.…”

Evolution of Carbon Shadow Prices in China’s Industrial Sector during 2003–2017: A By-Production Approach

“…A large body of literature has emphasized the importance of considering externalities in the form of undesirable outputs [24][25][26][27]. Unlike the free disposability assumption in models that take undesirable outputs as inputs or as desirable outputs, conventional DDF models [28][29][30][31] account for the negative externality of pollution by assuming weak disposability of desirable and undesirable outputs.…”

Effect of local government decision‐making competition on carbon emissions: Evidence from China's three urban agglomerations

“…The central government should strengthen the macrocontrol over local governments and intervene in the competition among local governments to a certain extent to prevent the emergence of vicious competition. Cooperation and information sharing among local governments should be strengthened to achieve Pareto optimality in the regional economy (Chen et al, 2019). The marketization of factor pricing should be accelerated, and a comprehensive system of factor price marketization should be built.…”

“…On the other hand, factor market distortion leads to an increase in the procurement cost of production factors and then influences enterprises' overall budgets. In the current situation in which financing is expensive, entrepreneurs reduce their R&D investment, resulting in a decrease in independent innovation and an increase in carbon emissions (Chen, Gao, Ma, & Song, 2019).…”