Mapping and modeling multiple benefits of energy efficiency and emission mitigation in China’s cement industry at the provincial level

Evaluating co-benefits of energy efficiency and air pollution abatement in China’s cement industry

Cutting air Pollution by Improving Energy Efficiency of China's Cement Industry

Co-benefits of energy efficiency improvement and air pollution abatement in the Chinese iron and steel industry

China’s cement and steel industry accounts for approximately half of the world’s total cement and steel production. These two industries are two of the most energy-intensive and highest carbon dioxide (CO2)-emitting industries and two of the key industrial contributors to air pollution in China. For example, the cement industry is the largest source of particulate matter (PM) emissions in China, accounting for 40 percent of its industrial PM emissions and 27 percent of its total national PM emissions. The Chinese steel industry contributed to approximately 20 percent of sulfur dioxide (SO2) emissions and 27 percent of PM emissions for all key manufacturing industries in China in 2013. In this study, we analyzed and projected the total PM and SO2 emissions from the Chinese cement and steel industry from 2010–2050 under three different scenarios: a Base Case scenario, an Advanced scenario, and an Advanced EOP (end-of-pipe) scenario. We used bottom-up emissions control technologies data and assumptions to project the emissions. In addition, we conducted an economic analysis to estimate the cost for PM emissions reductions in the Chinese cement industry using EOP control technologies, energy efficiency measures, and product change measures. The results of the emissions projection showed that there is not a substantial difference in PM emissions between the Base Case and Advanced scenarios, for both the cement and steel industries. This is mainly because PM emissions in the cement industry caused mainly by production process and not the fuel use. Since our forecast for the cement production in the Base Case and Advanced scenarios are not too different from each other, this results in only a slight difference in PM emissions forecast for these two scenarios. Also, we assumed a similar share and penetration rate of control technologies from 2010 up to 2050 for these two scenarios for the cement and steel industry. However, the Advanced EOP scenario showed significantly lower PM emissions for the cement industry, reaching to 1.7 million tons of PM in 2050, which is less than half of that in the other two scenarios. The Advanced EOP scenario also has the lowest SO2 emissions for the cement industry in China, reaching to 212,000 tons of SO2 in 2050, which is equal to 40 percent of the SO2 emissions in the Advanced scenario and 30 percent of the emissions in the Base Case scenario. The SO2 emission is mainly caused by fuel (coal) burning in cement kiln or steel processes. For the steel industry, the SO2 emissions of the Advanced EOP scenario are significantly lower than the other scenarios, with emissions declining to 323,000 tons in 2050, which is equal to 21 percent and 17 percent of the emissions of Advanced and Base Case scenarios in 2050, respectively. Results of the economic analysis show that for the Chinese cement industry, end-of-pipe PM control technologies have the lowest abatement cost per ton of PM reduced, followed by product change measures and energy efficiency measures, respectively. In summary, in order to meet Chinese national and regional air quality standards, best practice end-of-pipe emissions control technologies must be installed in both cement and steel industry and it must be supplemented by implementation of energy efficiency technologies and reduction of cement and steel production through structural change in industry.

Modeling energy efficiency to improve air quality and health effects of China’s cement industry

Urban residential energy switching in China between 1980 and 2014 prevents 2.2 million premature deaths

Pursuing air pollutant co-benefits of CO2 mitigation in China: A provincial leveled analysis

Industrial energy efficiency measures are proving financially viable, but the implementation rate is stagnating. This results in the need to develop a comprehensive and standardized methodology to assess the multiple benefits of energy efficiency measures in an industrial context. However, a comprehensive methodology to assess the multiple benefits of energy efficiency measures are omitted. The methodology, as presented in this study, was developed and validated based on nine case studies performed between 2016 and 2018 in the Swiss industrial sector. The aim is to close this gap with the introduction of a three-phase standard methodology, applicable to a wide range of industrial processes and energy efficiency measures. The three phases are further split into individual steps, each pursuing a specific goal in order to facilitate the implementation of energy efficiency measures. The first phase, the delimitation, aims at defining the system boundaries of the considered industrial process(es). The second phase, the assessment, involves the identification, the quantification, and the monetization of multiple benefits, as well as the qualitative assessment of non-monetizable multiple benefits. The last phase, the evaluation, focusses on the integration of the obtained results into the financial valuation of the energy efficiency measure and, therefore, on the cash flow analysis and the determination of the payback time under consideration of the monetizable multiple benefits. The study has shown that the consideration of monetizable multiple benefits may reduce the payback time of energy efficiency measures by up to 40–85%.

Abstract. Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 µm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67 µg m−3, 15 ppbv, and 470 ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China.

The GAINS (Greenhouse gas – Air pollution Interactions and Synergies) model quantifies the full DPSIR (demand-pressure-state-impact-response) chain for the emissions of air pollutants and greenhouse gases. To fulfill regional specific requirements of the GAINS model, we have studied and developed a cloud intelligent service system for calculating emissions and costs for reducing emissions at regional as well as global levels. In this paper, first we present a cloud intelligent conceptual model that is used to specify an application framework, namely GAINS cloud intelligent application framework. Using this application framework, first we build a global data warehouse called GAINS DWH World, then a class of regional data warehouses, e.g. GAINS DWH Europe, GAINS DWH Asia, etc, are specified and used for regional data analysis and cost optimization.KeywordsGAINSCloud intelligenceData Warehouse

This study aimed to create Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS)-Korea, an integrated model for evaluating climate and air quality policies in Korea, modeled after the international GAINS model. GAINS-Korea incorporates specific Korean data and enhances granularity for enabling local government-level analysis. The model includes source-receptor matrices used to simulate pollutant dispersion in Korea, generated through CAMx air quality modeling. GAINS-Korea's performance was evaluated by examining different scenarios for South Korea. The business as usual scenario projected emissions from 2010 to 2030, while the air quality scenario included policies to reduce air pollutants in line with air quality and greenhouse gas control plans. The maximum feasible reduction scenario incorporated more aggressive reduction technologies along with air quality measures. The developed model enabled the assessment of emission reduction effects by both greenhouse gas and air pollutant emission reduction policies across 17 local governments in Korea, including changes in PM2.5 (particulate matter less than 2.5 μm) concentration and associated benefits, such as reduced premature deaths. The model also provides a range of visualization tools for comparative analysis among different scenarios, making it a valuable resource for policy planning and evaluation, and supporting decision-making processes.

Economic impacts from PM2.5 pollution-related health effects in China's road transport sector: A provincial-level analysis

Integrating mitigation of air pollutants and greenhouse gases in Chinese cities: development of GAINS-City model for Beijing

Potentials of energy efficiency improvement and energy–emission–health nexus in Jing-Jin-Ji’s cement industry

IntroductionBlack carbon (BC) is a pollutant that illustrates strong links between climate warming and adverse health effects from air pollution. No standardised measurement technique for BC emissions has been implemented, making emissions and estimates highly uncertain. In this study, we evaluate two UK-based BC emission factor databases calculated using two distinct.Methodsthe National Atmospheric Emissions Inventory (NAEI) and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model database from IIASA. The scope of this investigation was limited to the 1 A (Fuel Consumption) NFR code, which comprised the largest BC-emitting activities in the UK. Comparisons were made between a reference NAEI value and a range of low (e.g., highest abatement, newest technology), medium, and high GAINS emission factors. The NAEI value sat outside the GAINS BC ranges across 64% of the selected 1 A sources, most evidently within industrial combustion. By comparison, PM2.5 and NOx emission factors within the same databases showed less frequent disagreement, with 26% and 46%, respectively, of the GAINS sources not overlapping with the NAEI reference. A complementary BC emissions estimate, using NAEI activity data, found the highest variance in emissions to be within industrial, domestic, and agricultural combustion sources. Overall, this paper highlights the need to understand the differences behind these BC emission factors and to bring them into closer alignment.