Performance analysis of CO(2) emissions and energy efficiency of metal industries in China

Carbon emissions in China's steel industry from a life cycle perspective: Carbon footprint insights

China is the most important steel producer in the world, and its steel industry is one of the most carbon-intensive industries in China. Consequently, research on carbon emissions from the steel industry is crucial for China to achieve carbon neutrality and meet its sustainable global development goals. We constructed a carbon dioxide (CO2) emission model for China's iron and steel industry from a life cycle perspective, conducted an empirical analysis based on data from 2019, and calculated the CO2 emissions of the industry throughout its life cycle. Key emission reduction factors were identified using sensitivity analysis. The results demonstrated that the CO2 emission intensity of the steel industry was 2.33 ton CO2/ton, and the production and manufacturing stages were the main sources of CO2 emissions, accounting for 89.84% of the total steel life-cycle emissions. Notably, fossil fuel combustion had the highest sensitivity to steel CO2 emissions, with a sensitivity coefficient of 0.68, reducing the amount of fossil fuel combustion by 20% and carbon emissions by 13.60%. The sensitivities of power structure optimization and scrap consumption were similar, while that of the transportation structure adjustment was the lowest, with a sensitivity coefficient of less than 0.1. Given the current strategic goals of peak carbon and carbon neutrality, it is in the best interest of the Chinese government to actively promote energy-saving and low-carbon technologies, increase the ratio of scrap steel to steelmaking, and build a new power system.

Evaluating the dynamic effects of mitigation instruments on CO2 emissions in China's nonferrous metal industry: A vector autoregression analysis

Reducing carbon dioxide (CO₂) emissions in China's nonferrous metal industry is important for reaching China's ambitious goals for carbon peaking and neutrality. Prior research identified several carbon abatement instruments for the industry. However, the dynamic influence of different mechanisms on CO₂ emissions in the industry remains unclear, and few studies have researched CO₂ emission reductions in two nonferrous metal related industrial subsectors: nonferrous metal ore mining and nonferrous metal smelting. This research evaluated the dynamic effect of abatement instruments on the CO₂ emissions in these two subsectors. The research discovered the factors that are highly linked with CO₂ emissions by using an enhanced Stochastic Impacts by Regression on Population, Affluence, and Technology model. The dynamic influence of these factors on CO₂ emissions in the two subsectors was investigated using a vector autoregressive model. Findings show that in the two subsectors, labour productivity and industrial value-added are the most important factors explaining CO₂ changes. The two variables have a negative long-term effect on CO₂ emissions in the nonferrous metal ores mining, and increase CO₂ emissions in the smelting of nonferrous metals. Improving energy efficiency in the nonferrous metal smelting industry decreases the CO₂ emissions only in the short term. In all sectors, lowering the electrical carbon emission factors and changing the energy structure using different techniques are expected to help reduce long-term CO₂ emissions. These results are critical for the Chinese government in creating long- and short-term energy plans for the nonferrous metal sector.

Energy policy paradox on environmental performance: The moderating role of renewable energy patents

The environmental and socioeconomic considerations of energy production have become crucial because of the increasingly complex relationship between energy and the environment. This study aims to develop possible mechanisms for perspectives on energy policy and the environment by exploring the mediating role of renewable energy patents. Non-radial data envelopment analysis and panel data models are applied using the panel data from 2010 to 2017 from 30 Chinese provinces. The results show an overall improvement in the environmental performance index (EPI) of China's provinces, but the average EPI is still relatively weak, with an average value between 0.44 and 0.52, which is far below the optimal value 1.. Furthermore, the econometric model offers evidence that provincial renewable energy and emission reduction policies positively impact the enhancement of EPI. The findings have several implications for energy and environmental policies.

The role of energy efficiency and energy diversity in reducing carbon emissions: empirical evidence on the long-run trade-off or synergy in emerging economies

Energy efficiency is often argued as a low-cost carbon emission mitigation strategy than energy diversity for emerging economies. Thus, deviations from carbon emission reduction goals due to diminished energy diversity may partially be compensated by energy efficiency improvements. This urgently requires an empirical analysis of carbon emission reduction impact of energy efficiency and energy diversity along with an investigation of any spillover effects. This study, therefore, investigates the long-run implications of energy efficiency and energy diversity on carbon emissions for emerging economies, covering the period 1990-2017. The long-run effect of energy efficiency on energy diversity and vice-versa is also examined to uncover any trade-off or synergy. The sample panel consists of the biggest seven emerging economies including, Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. The empirical analysis is based on an augmented environmental Kuznets curve model, estimated using the panel autoregressive distributed lag modeling technique. The results corroborate the argument that energy efficiency is an effective low-carbon strategy than energy diversity in the long run. Specifically, a 1% increase in energy efficiency and energy diversity reduces carbon emissions by 1.2% and 0.5%, respectively. Moreover, it is also revealed that the long-run impact of energy diversity on energy efficiency is negative, suggesting that a 1% increase in energy diversity reduces energy efficiency by 0.3%. This evidence uncovers a trade-off between these two strategies in the long-run. However, the long-run impact of energy efficiency on energy diversity is insignificant. These findings suggest that both low-carbon strategies must be promoted in tandem by considering the long-run trade-off between energy diversity and energy efficiency in emerging economies.

Will polycentric cities cause more CO2 emissions? A case study of 232 Chinese cities

From 2000 to 2010 China experienced rapid economic development and urbanization. Many cities in economically developed areas have developed from a single-center status to polycentricity. In this study, we used exploratory spatial data analysis (ESDA) to identify the population centers, which identified 232 cities in China as having urban centers. COMP was used to represent urban agglomeration, and POLYD (representing how far is the city's sub-centers to the main center), POLYC (representing the number of a city's centers), and POLYP (representing the population distributed between the main center and the sub-centers) were used to indicate urban polycentricity. Night light data were used to determine the CO₂ emissions from various cities in China. A mixed model was used to study the impact of urban aggregation and polycentric data on the CO₂ emission efficiency in 2000 and 2010. The study found that cities with higher compactness were distributed in coastal areas, and the cities with higher multicentricity were distributed in the Yangtze River Delta and Shandong Province. The more compact the city was, the less conducive it was to improving CO₂ emission efficiency. Polycentric development of the city was conducive to improving the CO₂ emission efficiency, but the number of urban centers had no significant relationship with the CO₂ emission efficiency. Our research showed that the compactness and multicentricity of the city had an impact on the CO₂ emission efficiency and provided some planning suggestions for the low carbon development of the city.

China's pilot emissions trading schemes and competitiveness: An empirical analysis of the provincial industrial sub-sectors

China's economic development has entered a "new normal" stage where economic growth has slowed down. In this context, China's local authorities and industry circles are particularly concerned about the impact of emissions trading scheme (ETS), China's first major market-based approach to control greenhouse gas emissions, on competitiveness. This paper contributes to the thin empirical studies on this issue from the perspective of China's provincial industrial sub-sectors. We divide the industry of each province into 37 sub-sectors and screen the actual industrial sub-sector coverage of China's pilot ETSs. Taking advantage of the rich information of sub-sector characteristics and the longitudinal structure of our dataset over 2005-2015, we use a combination of propensity score matching technique and difference-in-difference models to analyze the impact of China's pilot ETSs on gross industrial output value (GIOV) and employment. Empirical results indicate that China's pilot ETSs have exerted a negative impact on the GIOV, and production cut is still the major approach to achieve carbon emission reductions. We also find China's pilot ETSs have led to a significant decrease in employment of the covered industrial sub-sectors. Several robustness checks confirm our findings. Further, our discussions suggest that in the short term, China's pilot ETSs have not promoted the "decoupling" of carbon emissions and economic outputs in industrial sub-sectors. While achieving carbon emission reductions, the pilot ETSs have failed to avoid a negative impact on competitiveness. Lastly, we suggest that competent authorities should prudently tailor the quota allocation methods according to sectorial conditions and consider setting up compensation measures, and covered enterprises should incorporate low-carbon development concept and address the challenges proactively from the long run.

Quantitative analysis of the precise energy conservation and emission reduction path in China's iron and steel industry

A precise energy conservation and emission reduction (ECER) path in industrial sector contains two aspects: applying effective ECER measures and focusing on processes with significant ECER potential. However, most studies have investigated the ECER effects of an individual measure or only evaluated industrial-level ECER potential. Therefore, the objective of this study is to find a precise ECER path in China's iron and steel industry through quantitative analysis methods. First, this article adopts scenario analysis to simulate situations where different ECER measures are adopted and designs calculation methods to quantitatively evaluate the ECER effects in each scenario in 2020 and 2025. Second, through analysis of the application of ECER measures to certain processes, we calculate the ECER potential of different individual processes in the iron and steel industry. In addition, the conservation supply curve method and the quadrant method are used to measure the level of advanced technology application. The results show that: (1) for four types of ECER measures, the limitation of production output measure is most effective, contributing to 6.98% and 12.50% decreases in total industrial energy consumption and pollutant emissions in 2020 and 2025; moreover, the contribution of the adjustment of scale structure measure is comparatively low. (2) The sintering and ironmaking processes have strong ECER potential in 2020, while the steel making process also has high ECER potential in 2025. (3) 21 technologies are divided into 4 quadrants based on energy, popularity, and economic performance. In addition, we provide some suggestions for future ECER policies. In sum, this article provides an in-depth example of determining a precise ECER path in an important industry.

Heavy metal contamination assessment and partition for industrial and mining gathering areas

Industrial and mining activities have been recognized as the major sources of soil heavy metal contamination. This study introduced an improved Nemerow index method based on the Nemerow and geo-accumulation index. Taking a typical industrial and mining gathering area in Tianjin (China) as example, this study then analyzed the contamination sources as well as the ecological and integrated risks. The spatial distribution of the contamination level and ecological risk were determined using Geographic Information Systems. The results are as follows: (1) Zinc showed the highest contaminant level in the study area; the contamination levels of the other seven heavy metals assessed were relatively lower. (2) The combustion of fossil fuels and emissions from industrial and mining activities were the main sources of contamination in the study area. (3) The overall contamination level of heavy metals in the study area ranged from heavily contaminated to extremely contaminated and showed an uneven distribution. (4) The potential ecological risk showed an uneven distribution, and the overall ecological risk level ranged from low to moderate. This study also emphasized the importance of partition in industrial and mining areas, the extensive application of spatial analysis methods, and the consideration of human health risks in future studies.