• Energy Research

The coal-fired power sector is one of the major contributors to environmental problems and has great potential of air pollution abatement. This study employs Energy Conservation Supply Curves (ECSCs) combined with pollutant surcharge and health benefits to evaluate the environmental co-benefits of energy efficiency improvement in the coal-fired power sector. Health benefits and the pollution surcharge are considered as the environmental co-benefits that reduce costs of conserved energy (CCEs) in ECSCs. The health benefits of energy efficiency improvement are quantified using Intake Fraction method, while the pollutant surcharge is calculated based on the regulation. Three scenarios including a Business As Usual (BAU) scenario, an Energy Efficiency Improvement (EEI) scenario, and an Upgrading Standards and Incentive (USI) scenario is considered in a case study for Henan Province of China. Our results show that costs of conserved energy (CCEs) are reduced by 0.56 and 0.29 USD/GJ under the EEI and USI scenarios due to health benefits and pollutant surcharge reductions related to energy efficient technologies, respectively. In particular, health benefits account for 97% of the reductions in CCEs, while the pollutant surcharge only contributes 3%. Under the EEI and USI scenarios, in 2020, energy efficiency improvement reduces energy consumption in Henan’s coal-fired power sector by 3.3% and 3.5% compared with the BAU scenario, respectively. The EEI and USI scenarios indicates that health benefits of 1.5 × 109 and 2.4 × 109 USD are gained and the reductions of pollutant surcharges of 197 and 226 million USD are realized in 2020, respectively.

The coal-fired power sector is one of the major contributors to environmental problems and has great potential of air pollution abatement. This study employs Energy Conservation Supply Curves (ECSCs) combined with pollutant surcharge and health benefits to evaluate the environmental co-benefits of energy efficiency improvement in the coal-fired power sector. Health benefits and the pollution surcharge are considered as the environmental co-benefits that reduce costs of conserved energy (CCEs) in ECSCs. The health benefits of energy efficiency improvement are quantified using Intake Fraction method, while the pollutant surcharge is calculated based on the regulation. Three scenarios including a Business As Usual (BAU) scenario, an Energy Efficiency Improvement (EEI) scenario, and an Upgrading Standards and Incentive (USI) scenario is considered in a case study for Henan Province of China. Our results show that costs of conserved energy (CCEs) are reduced by 0.56 and 0.29 USD/GJ under the EEI and USI scenarios due to health benefits and pollutant surcharge reductions related to energy efficient technologies, respectively. In particular, health benefits account for 97% of the reductions in CCEs, while the pollutant surcharge only contributes 3%. Under the EEI and USI scenarios, in 2020, energy efficiency improvement reduces energy consumption in Henan’s coal-fired power sector by 3.3% and 3.5% compared with the BAU scenario, respectively. The EEI and USI scenarios indicates that health benefits of 1.5 × 109 and 2.4 × 109 USD are gained and the reductions of pollutant surcharges of 197 and 226 million USD are realized in 2020, respectively.

Moving to a sustainable industry and weaning electricity supply off coal are critical to mitigate ambient air pollution and climate change. This is particularly true in China which is globally the largest manufacturer and relies heavily on coal-fired electricity. Research that explores the linkages between industrial electricity use and the electricity supply sector to curb air pollution is limited. In this study, an integrated modeling framework is developed that quantifies the impact of industrial electricity savings on the evolution of the coal power plant fleet in China, and on air pollutants for the different power grids in the period 2016–2040. The framework includes a rich set of efficiency technologies and detailed unit-level information (geo-coordinates, thermal efficiency, environmental performance). We find that the reduced electricity load due to the industrial efficiency improvements can effectively scale down the coal power fleet, and most importantly allows closing the most polluting units. The potentials for electricity savings vary amongst the industrial sectors and provinces, resulting in significant heterogeneity of coal plant phaseout per power grid. Because energy-intensive industrial plants are mostly found in the North, Central and Northwest grids, these three grids provide 66% of the total displaced coal capacity. The closing of coal units leads to a variation in annual emission reductions per power grid of 13–85 kt-SO2, 19–129 kt-NOx, 3–17 kt-PM and 21–167 Mt-CO2, compared to business-as-usual emissions. The iron & steel, aluminium and chemical sectors, together contribute to 84% of the total electricity savings by 2040, and are thereby most important to target.

Moving to a sustainable industry and weaning electricity supply off coal are critical to mitigate ambient air pollution and climate change. This is particularly true in China which is globally the largest manufacturer and relies heavily on coal-fired electricity. Research that explores the linkages between industrial electricity use and the electricity supply sector to curb air pollution is limited. In this study, an integrated modeling framework is developed that quantifies the impact of industrial electricity savings on the evolution of the coal power plant fleet in China, and on air pollutants for the different power grids in the period 2016–2040. The framework includes a rich set of efficiency technologies and detailed unit-level information (geo-coordinates, thermal efficiency, environmental performance). We find that the reduced electricity load due to the industrial efficiency improvements can effectively scale down the coal power fleet, and most importantly allows closing the most polluting units. The potentials for electricity savings vary amongst the industrial sectors and provinces, resulting in significant heterogeneity of coal plant phaseout per power grid. Because energy-intensive industrial plants are mostly found in the North, Central and Northwest grids, these three grids provide 66% of the total displaced coal capacity. The closing of coal units leads to a variation in annual emission reductions per power grid of 13–85 kt-SO2, 19–129 kt-NOx, 3–17 kt-PM and 21–167 Mt-CO2, compared to business-as-usual emissions. The iron & steel, aluminium and chemical sectors, together contribute to 84% of the total electricity savings by 2040, and are thereby most important to target.

The industrial sector is a major energy consumer, responsible for about 35% of global energy use. In this article we focus on past developments of energy use and greenhouse gas emissions in industries and for the biggest energy consuming sectors we give an overview of energy saving opportunities. We find that about 60% of industrial energy use is consumed in four sectors, which are chemicals, iron and steel, cement and oil refineries. Coal is the most often used energy carrier (28%), followed by oil (26%), natural gas (19%) and electricity (18%). The implementation of best available technologies can lead to a reduction of about 20%–40% of energy use and greenhouse gas emissions, depending on country and sector.

The industrial sector is a major energy consumer, responsible for about 35% of global energy use. In this article we focus on past developments of energy use and greenhouse gas emissions in industries and for the biggest energy consuming sectors we give an overview of energy saving opportunities. We find that about 60% of industrial energy use is consumed in four sectors, which are chemicals, iron and steel, cement and oil refineries. Coal is the most often used energy carrier (28%), followed by oil (26%), natural gas (19%) and electricity (18%). The implementation of best available technologies can lead to a reduction of about 20%–40% of energy use and greenhouse gas emissions, depending on country and sector.

Using a sample of 18 prefecture-level cities in Henan province, this study explored the regional allocation of energy intensity reduction targets from the following three viewpoints: equity principle with common but differentiated responsibilities; intensity reduction target fulfillment; and economic differences and reduction potential among regions. Based on a preliminary decomposition model, an analytic hierarchy process (AHP) and Ward's hierarchical clustering, an intensity allocation method is proposed. First, the preliminary regional decomposition scheme is presented via the preliminary decomposition model. Then, a multi-criteria evaluation system consisting of four layers and covering 13 evaluation indicators is developed via the AHP method, and the evaluation results are analyzed via the cluster method to further improve the preliminary scheme. As decision makers may have different preferences when allocating the reduction burden, we allocate different weights to the indicators and analyze the results using a sensitivity analysis. The clustering results indicate that the 18 regions of Henan are divided into five categories, and each category has its own significant characteristics. Regions with high obligation and potential should share the largest reduction burden. The allocation results show that seven regions, including Zhengzhou and Luoyang, are expected from 2016 to 2020 to exceed the provincial average decrease rate of 16%.

Using a sample of 18 prefecture-level cities in Henan province, this study explored the regional allocation of energy intensity reduction targets from the following three viewpoints: equity principle with common but differentiated responsibilities; intensity reduction target fulfillment; and economic differences and reduction potential among regions. Based on a preliminary decomposition model, an analytic hierarchy process (AHP) and Ward's hierarchical clustering, an intensity allocation method is proposed. First, the preliminary regional decomposition scheme is presented via the preliminary decomposition model. Then, a multi-criteria evaluation system consisting of four layers and covering 13 evaluation indicators is developed via the AHP method, and the evaluation results are analyzed via the cluster method to further improve the preliminary scheme. As decision makers may have different preferences when allocating the reduction burden, we allocate different weights to the indicators and analyze the results using a sensitivity analysis. The clustering results indicate that the 18 regions of Henan are divided into five categories, and each category has its own significant characteristics. Regions with high obligation and potential should share the largest reduction burden. The allocation results show that seven regions, including Zhengzhou and Luoyang, are expected from 2016 to 2020 to exceed the provincial average decrease rate of 16%.