A considerable body of economic literature shows the adverse economic impacts of oil-price shocks for the developed economies. However, there has been a lack of empirical study of this kind on China and other developing countries. This paper attempts to fill this gap by answering how and to what extent oil-price shocks impact China's economy, emphasizing on the price transmission mechanisms. To that end, we develop a structural vector auto-regressive model. Our results show that an oil-price increase negatively affects output and investment, but positively affects inflation rate and interest rate. However, with the differentiated price control policies for materials and intermediates on the one hand and final products on the other hand in China, the impact on real economy, represented by real output and real investment, lasts much longer than that to price/monetary variables. Our decomposition results also show that the short-term impact, namely output decrease induced by the cut of capacity-utilization rate, is greater in the first one to two years, but the portion of the long-term impact, defined as the impact realized through an investment change, increases steadily and exceeds that of short-term impact at the end of the second year. Afterwards, the long-term impact dominates, and maintains for quite some time.
Air pollution reduction policies can also mitigate CO2 emissions simultaneously in the industrial sector, but the extent of these co-benefits is understudied. We analyse the potential co-benefits for SO2, NOx, particulate matter (PM) and CO2 emissions reduction in major industrial sectors in China. We construct and analyse a firm-level database covering more than 75 thousand observations and scenario simulations are used to estimate the co-benefits. The findings show that substantial co-benefits could be achieved with three specific interventions. Energy intensity improvement can reduce SO2, NOx, PM and CO2 emissions by 26-44%, 19-44%, 25-46% and 18-50% respectively. Reductions from scale structure adjustment such as phasing out small firms and developing large ones can amount to 1-8%, 1-6%, 2-20% and 0.2-3%. Electrification can reduce emissions by 19-25%, 4-28%, 20-29% and 11-12% if the share of electricity generated from nonfossil fuel sources is 70%. The former two interventions have already been put into practice while the third intervention is regarded as a significant contributor for realizing China's carbon neutrality target. Since firm heterogeneity is the essential source for realizing the co-benefits and it directly determines the magnitude of the co-benefits, stricter and sensible environmental policies targeting industrial firms can accelerate China's sustainable transformation.China is seizing opportunities to achieve its climate commitment to the Pairs Agreement of UNFCCC, while the overall energy-related CO2 emission continues to rise after a small trough in 2016. Domestically, China has battled air pollution for more than thirty years 1 . Although great achievements have been made, China is still facing severe environmental challenges 2,3 . In 2018, only 121 out of 338 cities at and above the prefecture level met the national air quality standard 4 . Among all emission sources, industrial sectors contributed more than 80% of national sulphur dioxide (SO2) emission, more than 60% of national nitrous oxide (NOx) emissions 5 , and more than 80% of national carbon dioxide (CO2) emissions 6 ; therefore, it is still the first priority to strengthen
A considerable body of economic literature shows the adverse economic impacts of oil-price shocks for the developed economies. However, there has been a lack of empirical study of this kind on China and other developing countries. This paper attempts to fill this gap by answering how and to what extent oil-price shocks impact China's economy, emphasizing on the price transmission mechanisms. To that end, we develop a structural vector auto-regressive model. Our results show that an oil-price increase negatively affects output and investment, but positively affects inflation rate and interest rate. However, with the differentiated price control policies for materials and intermediates on the one hand and final products on the other hand in China, the impact on real economy, represented by real output and real investment, lasts much longer than that to price/monetary variables. Our decomposition results also show that the short-term impact, namely output decrease induced by the cut of capacity-utilization rate, is greater in the first one to two years, but the portion of the long-term impact, defined as the impact realized through an investment change, increases steadily and exceeds that of short-term impact at the end of the second year. Afterwards, the long-term impact dominates, and maintains for quite some time.
Estimating the impact of climate change on energy use across the globe is essential for analysis of both mitigation and adaptation policies. Yet existing empirical estimates are concentrated in Western countries, especially the United States. We use daily data on household electricity consumption to estimate how electricity consumption would change in Shanghai in the context of climate change. For colder days <7 °C, a 1 °C increase in daily temperature reduces electricity consumption by 2.8%. On warm days >25 °C, a 1 °C increase in daily temperatures leads to a 14.5% increase in electricity consumption. As income increases, households’ weather sensitivity remains the same for hotter days in the summer but increases during the winter. We use this estimated behavior in conjunction with a collection of downscaled global climate models (GCMs) to construct a relationship between future annual global mean surface temperature (GMST) changes and annual residential electricity consumption. We find that annual electricity consumption increases by 9.2% per +1 °C in annual GMST. In comparison, annual peak electricity use increases by as much as 36.1% per +1 °C in annual GMST. Although most accurate for Shanghai, our findings could be most credibly extended to the urban areas in the Yangtze River Delta, covering roughly one-fifth of China’s urban population and one-fourth of the gross domestic product.
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