Abstract
China has become the largest wind power installation market in the world, and on such a large scale its wind power industry contributes to the sustainability of electricity generation and reduction of carbon emissions, yet has problems such as wind curtailment, insufficient wind power consumption, and regional disparities. Thus, this research uses an epsilon-based measure (EBM) data envelopment analysis (DEA) model to evaluate and compare wind power electricity generation efficiency and CO2 emission reduction efficiency in the eastern, central, western, and northeastern regions of China for the period 2013–2017. The empirical results show that the nation’s overall wind power efficiency presents a significant upward trend, in which the western and northeastern regions have increased the most, while the east region has increased the least. Technical inefficiency is mainly due to diseconomies of scale in China’s wind power industry. Moreover, CO2 emission reduction in the four regions exhibits high efficiency, and the regions’ efficiencies are very consistent with that of installed capacity efficiency. Finally, this study’s policy implications are that industry development plans should be made according to local conditions as well as cross-regional trade of wind power electricity and that the upgrading of wind power generation capacity should be encouraged.
Highlights
Electricity-related carbon emissions account for over 40% of global carbon emissions and are the main contributor to global warming (Wei et al, 2020)
Through a decomposition of technical efficiency in China’s wind power industry, this study presents that the annual average pure technical efficiency of its wind power industry is higher than annual average scale efficiency, which is consistent with Dong and Shi (2019) and Wang et al (2020)
Comparing the overall efficiency value of China’s wind power industry calculated by epsilon-based measure (EBM) with the technical efficiency value of this industry calculated by the radial data envelopment analysis (DEA) model, we find that the efficiency value calculated by EBM (0.617) is lower than that calculated by the radial DEA model (0.788)
Summary
Electricity-related carbon emissions account for over 40% of global carbon emissions and are the main contributor to global warming (Wei et al, 2020). In 2014, global newly-added capacity of renewable energy power generation exceeded newly-installed capacity of conventional energy power generation for the first time, marking a structural change in the global power system. Among various kinds of renewable energy power generation, wind power has gradually attracted people’s attention due to the huge amounts of available wind, its high cost-effectiveness rate (Kaldellis, 2011; Dawn et al, 2019), and its large potential for energy savings and emission reduction (Yang and Chen 2013). For the achievement of “carbon peak and carbon neutralization” and sustainability of power production, China is actively developing its own renewable energy power generation industry. Because of the country’s large wind energy reserves and wide distribution, wind power generation has been vigorously promoted
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