• Energy Research

Promoting energy efficiency in iron and steel production provides opportunities for mitigating environmental impacts from this energy-intensive industry. Energy efficiency technologies differ in investment costs, fuel-saving potentials, and environmental performance. Hence the decision-making of the adoption strategy needs to prioritize technological combinations concerning these multi-dimensional objectives. To address this problem, this study proposes a hybrid multi-criteria decision-support model for adopting energy efficiency technologies in the iron and steel industry. The modeling framework integrates a linear programming model that determines the optimal technology adoption rates based on the techno-economic, energy, and environmental performance details and an interactive multi-criteria model analysis tool for diverse modeling environments. A real case study was performed in which a total number of 56 energy efficiency technologies were investigated against various criteria concerning economics, energy, and environmental performances. The results examine the tradeoffs and synergies were examined with regard to seven criteria. A balanced solution shows that a total investment of 13.4 billion USD could save 2.51 Exajoule fuel consumption, cut 67.4 million tons (Mton) CO2 emissions, and reduce air pollution of 1.5 Mton SO2, 1.41 Mton NOx, and 0.86 Mton PM, respectively. The case study demonstrates the effectiveness and applicability of the proposed multi-criteria decision-making support framework.

Our societies are continuously grappling with how to achieve rapid economic growth while minimizing the challenges of environmental sustainability. In this avenue, numerous studies have contributed towards investigating socio-economic factors and developing policies targeting environmental pressures (EPs). While previous studies have tended to focus on the individual driving forces of EPs, the consideration of the co-benefits and trade-offs among different EPs and policies have been considerably overlooked. In China, previous studies have mostly engaged these issues at the national level and have overlooked the regional socio-economic characteristics – this presents a mismatch between regional policy applications and average national level research findings. Towards this end, this study examines the co-benefits and trade-offs of eight EPs in Zhejiang during the 2007–2015 period. Our findings revealed strict co-benefits in reductions of all eight EPs due to intensity changes as well as trade-offs due to changes in final demand structure and final demand composition. Sectoral results show that only the Non-Ferrous Metal Ores sector has strict co-benefits among all EPs from the production perspective, while eight sectors have strict co-benefits from the consumption perspective mainly including the Mining and Washing of Coal, Ferrous Metal Ores, Electric Power and Heat Power sectors. Our findings suggest important policy implications associated with utilizing co-benefits and avoiding trade-offs for EP mitigation: making full use of all driving forces, strengthening intersectoral coordination, and establishing a joint evaluation mechanism among different sectors.

Abstract This study deals with technology evaluation by employing the data envelopment analysis (DEA) methodologies from a life-cycle analysis (LCA) perspective. Aluminum production is associated with high electricity consumption. Thus, the environmental pressures of the whole production chain largely rely on the electricity generation process. This is particularly true for China, because coal-fired power generation dominates China’s electricity supply. Many of aluminum plants in China consume electricity which is directly supplied by coal-fired plants. Given these facts, LCA perspective in this study is believed to be of distinctive significance. Therefore, this paper discusses the process requiring high-energy consumption and causing high pollution from the LCA perspective and combines data envelopment analysis with LCA to evaluate and select energy-saving technological routes throughout for the complete supply chain. In total, 16 technological combinations are considered, and three models are established for different sets of efficiency indicators: energy performance indicator, energy performance indicator with undesirable output, and total performance indicator. Moreover, four scenarios with different policy considerations are designed: baseline scenario, subsidy scenario, low-carbon-price scenario, and high-carbon-price scenario. The results show that upon only considering energy input and output at the same price, a power generation mode has the greatest impact on energy consumption. After considering undesirable output, such as pollutant emissions, the technological routes including coal-fired power generation have the worst performance. Considering all inputs and outputs with the same price, the most crucial factor affecting efficiency is cost, followed by anode; subsidies have a low effect. For the four scenarios, two technological routes including a biomass-integrated gasification combined cycle power generation and inert anode rank in the top 50% decision-making units with three efficiency indices. This paper proposes an effective decision-making method for evaluating and selecting energy-saving technology routes for the aluminum industry, which can be easily applied to other sectors with similar characteristics. This provides a generic framework for a profound analysis to promote clean production and environmental sustainability.

For China to achieve carbon emissions peak and neutrality, the structural adjustment of both its economy and energy system is essential. In this study, a multi-objective optimization model based on the Input-Output approach is built to coordinate diverse policy targets vis-à-vis GDP growth, carbon emissions reduction, employment, and energy-saving of China from 2020 to 2030. The optimal structural adjustment pathways of China's economy, reflecting a high-resolution of available electricity generation technologies, under four policy preferences, are planned and the co-benefits and trade-offs among multiple policy targets are detected. Our results reveal that while the energy-saving preference is more likely to hinder GDP growth (by −190 trillion yuan) and employment levels (by −60 million jobs), however, this preference is conducive to carbon emissions reduction (by −2.6 billion tons). Furthermore, our findings reveal that although the low-carbon preference does not undermine employment levels, however, it will restrain GDP growth (by 109 trillion yuan). The integrated management of multiple policy targets would require the country's industrial structure to increase the proportion of low-carbon to total electricity generation to account for 71% by 2030 and the proportion of the services sector to the whole economy to account between 42% and 51% by 2030.

Modeling and optimizing long-term energy systems can provide solutions to various energy and environmental policies involving public-interest issues. The conventional optimization of long-term energy system models focuses on a single economic goal. However, the increasingly complex demands of energy systems necessitate the comprehensive consideration of multiple dimensional objectives, such as environmental, social, and energy security. Therefore, a multi- objective optimization of long-term energy system models has been developed. Herein, studies pertaining to the multi- objective optimization of long-term energy system models are summarized; the optimization objectives of long-term energy system models are classified into economic, environmental, social, and energy security aspects; and the multi-objective optimization methods are classified and explained based on the preferential expression of decision makers. Finally, the key development direction of the multi-objective optimization of energy system models is discussed.

Because of its important role in China, many scholars have addressed the decarbonization of the Yangtze River Delta Region (YRDR). However, little work has been conducted on appropriate ways to transform the YRDR power system into a carbon-neutral system. This study develops an optimization model to explore the optimal pathways toward a carbon-neutral power system in the YRDR by 2060. In addition to traditional power generation technologies, the model includes carbon tax (or carbon emission cost), carbon capture and storage (CCS), forest carbon sink (FCS), renewable energy, and energy imports from outside the YRDR. The main findings are as follows: (1) in the business-as-usual (BAU) scenario, the YRDR's power system could reach its carbon peak by 2030, but it would not achieve carbon neutrality by 2060; (2) in the carbon neutrality scenario, FCS could mitigate 88 % of the carbon emissions of the YRDR power system; and (3) a high proportion of renewable energy could help transform the YRDR's power system to a carbon-neutral one, but would increase the cost by 44.8 %. The main policy implication is that implementing a carbon tax and promoting renewable energy, FCS, CCS, and other carbon dioxide removal (CDR) technologies should be considered together to transform the YRDR power system.

Beijing has implemented air pollution control policies and transitioned its energy system with lower carbon emissions to tackle severe air pollution. However, further advancing to a carbon–neutral future necessitates comprehensive measures far beyond the air-quality-oriented policies. This study aims to explore and compare different transition strategies of the Beijing energy system to achieve carbon neutrality and assess the associated air pollution reduction co-benefits by using an integrated modelling framework consisting of an energy system model MESSAGEix-Beijing and an air quality assessment model GAINS. Three scenarios are developed, namely, baseline (BS), indigenous (IND), and imported electricity-dependent (EIMP). The two distinct low-carbon pathways differ in cost-optimal technological solutions and the associated impacts of air pollution reduction. Compared to the BS scenario, the IND and EIMP scenarios could reduce the carbon dioxide emissions in Beijing by 94 to 96% in 2050 and achieve substantial air pollution co-benefits. In the IND scenario, the NOx, SO2, and PM2.5 emissions would decrease by 50%, 84%, and 30% in 2050, respectively. Importing full electricity from other provinces, as indicated by the EIMP scenario, would achieve even higher emissions reduction for air pollutants. The results highlight the necessity for concerted regional development of adjacent provinces to avoid the spillover of carbon emissions and air pollution.

Abstract China is the world's largest cement producer, contributing to 60% of the global total. Jiangsu province takes the lead of cement production among China's provinces, contributing to 8.4% of the national total cement output. In this study, a geo-graphical information system-based energy model is developed to assess the potential of energy savings and associated mitigation of CO2 and air pollutant emissions in Jiangsu's cement industry during 2015–2030. Results show that 1) compared to 2015, energy consumption in the baseline scenario will decrease by 54% at the provincial level. Economical energy saving potential for 2030 is around 50 PJ, which equals to 35% of energy use in the baseline in 2030. 2) At the city level, Changzhou, Wuxi, and Xuzhou are top three cities in terms of energy saving potential. 3) The economical CO2 emission reductions will decrease by 4.4 Mt in 2030, while the emissions of PM and NOx would decline by 30% and 56%, respectively. This study will help policy makers develop integrated policies to support the coordinated development of Jiangsu and can also enhance the effectiveness of the implementation of joint prevention and control of atmospheric pollution to improve regional air quality.