Emission inventories are critical to understanding the sources of air pollutants, but have high uncertainties in China due in part to insufficient on-site measurements. In this study, we developed a method of examining, screening and applying online data from the country's improving continuous emission monitoring systems (CEMS) to reevaluate a “bottom-up” emission inventory of China's coal-fired power sector. The benefits of China's current national emission standards and ultra-low emission policy for the sector were quantified assuming their full implementation. The derived national average emission factors of SO₂, NO_x and particulate matter (PM) were 1.00, 1.00 and 0.25 kg/t-coal respectively for 2015 based on CEMS data, smaller than those of previous studies that may not fully recognize improved emission controls in recent years. The annual emissions of SO₂, NO_x and PM from the sector were recalculated at 1321, 1430 and 334 Gg respectively, 75%, 63% and 76% smaller than our estimates based on a previous approach without the benefit of CEMS data. The results imply that online measurement with proper data screening can better track the recent progress of emission controls. The emission intensity (the ratio of emissions to economic output) of Northwest China was larger than that of other regions, attributed mainly to its less intensive economy and industry. Transmission of electricity to more-developed eastern provinces raised the energy consumption and emissions of less-developed regions. Judged by 95 percentiles of flue-gas concentrations measured by CEMS, most power plants met the current national emission standards in 2015 except for those in Northwest and Northeast China, while plants that met the ultra-low emission policy were much scarcer. National SO₂, NO_x and PM emissions would further decline by 68%, 55% and 81% respectively if the ultra-low emission policy can be strictly implemented, implying the great potential of the policy for emission abatement.

The continuous entry of new firms and exit of old ones might have substantial effects on productivity of energy supply. Since China is the world’s largest energy producer, productivity of energy supply in China is a significant issue, which affects sustainability. As a technical application, this paper investigates the productivity and dynamic changes of Chinese coal mining firms. We find that the total factor productivity (TFP) growth of coal supply in China is largely lagging behind the growth rate of coal production. The entry and exit of non-state-owned enterprise (non-SOE) partially provide explanation for the dynamic change of aggregate TFP. Specifically, non-state owned entrants induced by the coal price boom after 2003, which had negative effects on TFP of energy supply, while the exit of non-SOEs had positive effects. Furthermore, there is regional heterogeneity concerning the effects of entry and exit on energy supply productivity. More entrants induced by coal price boom are concentrated in non-main production region (non-MPR), while more exits are located in MPR due to the government’s enforcement. This provides explanation for the phenomena that productivity of energy supply in MPR gradually surpasses that in non-MPR. We also anticipate our paper to enhance understanding on the energy supply-side, which might further help us make informed decisions on energy planning and environmental policies.

Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments.

We explore how water pollution policy reforms in China could reduce industrial wastewater pollution with minimum adverse impact on GDP growth. We use a multi-sector dynamic Computable General Equilibrium (CGE) model, jointly developed by Harvard University and Tsinghua University, to examine the long-term impact of pollution taxes. A firm-level dataset of wastewater and COD discharge is compiled and aggregated to provide COD-intensities for 22 industrial sectors. We simulated the impact of 4 different sets of Pigovian taxes on the output of these industrial sectors, where the tax rate depends on the COD-output intensity. In the baseline low rate of COD tax, COD discharge is projected to rise from 36 million tons in 2018 to 48 million in 2030, while GDP grows at 6.9% per year. We find that raising the COD tax by 8 times will lower COD discharge by 1.6% by 2030, while a high 20-times tax will cut it by 4.0%. The most COD-intensive sectors—textile goods, apparel, and food products—have the biggest reduction in output and emissions. The additional tax revenue is recycled by cutting existing taxes, including taxes on profits, leading to higher investment. This shift from consumption to investment leads to a slightly higher GDP over time.