Structural and technological determinants of carbon intensity reduction of China's electricity generation

Towards a low-carbon future for China's power supply chain: Critical sectors identification and scenario analysis

The power sector is a significant contributor to global carbon emissions and has received widespread attention from scholars; however, the path to achieving supply chain-wide carbon reductions in China from a provincial perspective remains unclear. This study combined multi-regional input-output and betweenness-based methods to identify the critical upstream sectors that indirectly drive large amounts of carbon emissions through power supply chains. The point source data of coal-fired units were collected to ensure the accuracy of the disaggregated input-output table. In addition, a scenario analysis was conducted to examine the effects of different electricity policy combinations on supply chain-wide emissions during the 14th Five-Year Plan (FYP). Our findings indicate that the embodied carbon intensity of the coal-fired power sector in Northwest China is among the highest in the country, ranging from 36.39 to 82.10 tons/10000 CNY. Therefore, the shift of the power sector to Western China during the 14th FYP will partially offset the positive emission reduction effect of the structural transformation of the power system. To achieve a low-carbon power supply chain, it is necessary to improve the production efficiency of critical transmission sectors and the low-carbon technology levels of major emitting sectors. Our results provide valuable insights for provincial governments to plan low-carbon transformation paths for the power sector.

The COVID-19 pandemic and energy transitions: Evidence from low-carbon power generation in China

The Corona Virus Disease 2019 (COVID-19) has led to a decline in carbon emissions or an improvement in air quality. Yet little is known about how the pandemic has affected the "low-carbon" energy transition. Here, using difference-in-differences (DID) models with historical controls, this study analyzed the overall impact of COVID-19 on China's low-carbon power generation and examined the COVID-19 effect on the direction of the energy transition with a monthly province-specific, source-specific dataset. It was found that the COVID-19 pandemic increased the low-carbon power generation by 4.59% (0.0648 billion kWh), mainly driven by solar and wind power generation, especially solar power generation. Heterogeneous effects indicate that the pandemic has accelerated the transition of the power generation mix and the primary energy mix from carbon-intensive energy to modern renewables (such as solar and wind power). Finally, this study put forward several policy implications, including the need to promote the long-term development of renewables, green recovery, and so on.

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000-2019

China has been reported as the world's largest carbon emitter, facing a tough challenge to meet its carbon peaking goal by 2030. Reducing the carbon intensity of energy-intensive industries (EIICI) is a significant starting point for China to achieve its emission reduction targets. To decompose the overall target into regions, understanding the spatiotemporal differences and drivers of carbon intensity is a solid basis for the scientific formulation of differentiated regional emission reduction policies. In this study, the spatiotemporal differences of EIICI are described using the panel data of 30 provinces in China from 2000 to 2019, and a spatial econometric model is further adopted to analyze its drivers. As indicated by the results: (1) from 2000 to 2019, China's EIICI tended to be reduced continuously, and the spatial differences at the provincial and regional levels expanded continuously, thus revealing the coexistence of "high in the west and low in the east" and "high in the north and low in the south" spatial patterns. (2) There is a significant spatial autocorrelation in the EIICI, characterized by high and high agglomeration and low and low agglomeration types. Moreover, the spatial spillover effects are denoted by a 1% change in the local EIICI, and the adjacent areas will change by 0.484% in the same direction. (3) Technological innovation, energy structure, and industrial agglomeration have direct and indirect effects, thus affecting the local EIICI and the adjacent areas through spatial spillover effects. Economic levels and firm sizes only negatively affect the local EIICI. Environmental regulation merely has a positive effect on adjacent areas. However, the effect of urbanization level on EIICI has not been verified, and the effect of urbanization level on the EIICI has not been verified. The results presented in this study show a scientific insight into the reduction of EIICI in China. Furthermore, policymakers should formulate differentiated abatement policies based on dominant drivers, spatial effects, and regional differences, instead of implementing similar policies in all provinces.

An Asymmetric Nexus between Urbanization and Technological Innovation and Environmental Sustainability in Ethiopia and Egypt: What Is the Role of Renewable Energy?

The present study investigates the nexus between urbanization, technological innovation, renewable energy consumption, and environmental quality in Egypt and Ethiopia from 1980 to 2020 by employing symmetric and asymmetric frameworks. Referring to symmetric assessment, the coefficient of renewable energy consumption and technological innovation revealed a negative and statistically significant tie with environmental sustainability, valid for both proxies. Study findings suggest that clean energy integration and technological innovations in the economy decrease environmental adversity by reducing carbon emissions and ecological blames. Although the elasticity of urbanization has documented a positive and statistically significant connection with environmental sustainability, the conclusion is valid for both models. Second, in the long run, the asymmetric shocks of renewable energy consumption and technological innovation have exposed a negative and statistically significant tie to environmental sustainability, whereas in the case of urbanization, the asymmetric shocks unveiled a positive and statistically significant association to environmental sustainability. Third, the study revealed that the feedback hypothesis explains the relationship between technological innovation and environmental sustainability [TI←→EF] in Egypt and ecological footprint and urbanization in Egypt and Ethiopia. Moreover, unidirectional causality runs from ecological footprint to renewable energy consumption in Egypt and Ethiopia.

Inter-Provincial Electricity Trading and Its Effects on Carbon Emissions from the Power Industry

Electricity trading is an effective measure to minimize carbon emissions and alleviate the imbalance between reverse distribution of regional energy resources and power load. However, the effects of China’s electricity trading on carbon emissions have not been fully explored due to lack of complete and balanced inter-provincial power transmission data. Therefore, the electricity generation–consumption downscaling model, logarithmic mean Divisia index (LMDI) model, and random forest clustering algorithm within a general framework were used in the present study to explore the effect of electricity trading on level of carbon emissions. Comprehensive inter-provincial electricity transmission data were generated, driving factors including electricity imports and exports were decomposed at the national and provincial scales, and clustered provincial policy implications were evaluated. The results revealed that: (i) although economic activities were the main driving factor for increase in carbon emissions at the national level, 382.95 million tons carbon emissions were offset from 2005 to 2019 due to inter-provincial electricity importation, whereas electricity export increased carbon emission by 230.30 million tons; (ii) analysis at the provincial level showed that electricity exports from Sichuan and Yunnan provinces accounted for more than 20% of the nation’s total electricity flow. Notably, this high level of exports did not significantly increase carbon emissions in these provinces owing to the abundant hydropower resources; (iii) emission reductions were only observed at the national level if the carbon intensity of the exporting provinces was lower compared with that of importing provinces, or if the electricity trading was generated from renewable sources; (iv) the effect of electricity import on emissions reduction was markedly higher relative to the effect of electricity export in most provinces, which reflected the actual situation of sustaining optimization of electricity generation structure in provincial grids of China. These findings provide a basis for decision makers to understand the contributions of electricity trading to the changes in carbon emissions from electricity generation, as well as form a foundation to explore practicable carbon emission mitigation strategies in the power industry.

Examining the Effects of Installed Capacity Mix and Capacity Factor on Aggregate Carbon Intensity for Electricity Generation in China

Promoting technological advancements and energy transitions in electricity generation are crucial for achieving carbon reduction goals. Some studies have examined the effectiveness of these measures by analysing the driving forces of "aggregate carbon intensity" (ACI) change. However, only a few studies have considered the effect of the installed capacity mix and capacity factor. Moreover, such analysis has never been applied at China's provincial level after 2015. To alleviate this gap, our study applied a temporal and multi-regional spatial IDA-LMDI model to analyse the driving factors of ACI changes and disparities among the provinces of China from 2005 to 2019. The model notably includes the effects of the installed capacity mix, thermal capacity factor, and overall capacity factor. The analysis revealed that the decline in China's ACI was diminished after 2015, while an ACI rebound was identified in five provinces. The changes in the ACI from 2015 to 2019 were mainly driven by the effect of the installed capacity mix rather than by the thermal efficiency and thermal capacity factor. The overall capacity factor was the only factor with a negative impact on the ACI change. We also found that its combined effect with the thermal capacity factor on increasing ACI can offset the effect of the installed capacity mix by reducing the ACI in provinces with significant additions of renewable energy installed capacity. The analysis of the influencing factors on the provincial ACI differences revealed that the share of hydropower installed capacity was significant. Moreover, the thermal efficiency and thermal capacity factor both played key roles in the ACI disparities in northeast, northwest, and central China. Overall, this study paves the way for data-driven measures of China's carbon peak and carbon neutrality goals by improving the capacity factor of wind and solar power, leveraging the critical impact of hydropower, and narrowing the differences in the thermal power sector among provinces.