• Energy Research

Abstract This paper aims to examine the environmental performance of the multi-crystalline (multi-Si) photovoltaic installations by conducting a life cycle assessment (LCA) of a typical 1-Megawatt on-grid ground-mounted solar power station in China. An energy payback time calculation will be presented with some further suggestions. After a thorough study of the LCA of solar power station, the boundary of the goal is clearly presented, making it feasible to study the total input and the annual output of the system. Specifically speaking, the total energy input, including the energy input of the module manufacturing and the energy input of balance of system (BOS) is 19.5548 x10 6 MJ, while the annual energy output is calculated to be 8.328 x 10 6 MJ. Thus the energy payback time (EPBT) is 2.3 years, revealing the conclusion that the establishment of the solar power station would contribute to a clean usage for more than 27 years, given the assumption of a 30-year operation period. Therefore, the solar power station is much more environmental friendly compared to the traditional fossil fuel systems.

High-precision CO2 emission data by sector are of great significance for formulating CO2 emission reduction plans. This study decomposes low-precision energy consumption data from China into 149 sectors according to the high-precision input–output (I–O) table for 2017. An economic I–O life cycle assessment model, incorporating sensitivity analysis, is constructed to analyze the distribution characteristics of CO2 emissions among sectors. Considering production, the electricity/heat production and supply sector contributed the most (51.20%) to the total direct CO2 emissions. The top 10 sectors with the highest direct CO2 emissions accounted for >80% of the total CO2 emissions. From a demand-based perspective, the top 13 sectors with the highest CO2 emissions emitted 5171.14 Mt CO2 (59.78% of the total), primarily as indirect emissions; in particular, the housing construction sector contributed 23.97% of the total. Based on these results, promoting decarbonization of the power industry and improving energy and raw material utilization efficiencies of other production sectors are the primary emission reduction measures. Compared with low-precision models, our model can improve the precision and accuracy of analysis results and more effectively guide the formulation of emission reduction policies.

With the growing cost of carbon emissions reduction, the application of industrial restructuring to suppress carbon emissions is becoming more attractive. By constructing an input-output optimization model, this study explored how industrial restructuring helps megacities synergistically achieve carbon peak and high-quality development. The results showed that through contributing 164.4% of the reduction in emissions from 2020 to 2025, industrial structure optimization significantly inhibited the growth of carbon emissions; From 2020 to 2025, the manufacturing structure continued to be high-end, which resulted in a reduction in industrial carbon emissions by 10.3%; through vigorous development of the low-carbon service industry, the carbon emission of the service industry would continue to slow down at an average annual rate of 2.4%. Industrial premiumization and the low-carbonization of the modern service sector are the key driving forces for Shenzhen to achieve low-carbon transformation. The results also showed that the power and retail sectors are the most important for emissions reduction. This study can provide a roadmap for megacities on how to explore potential emission reduction via optimizing their economic structure to help them achieve their carbon emissions peak.

To realize the sustainable development of energy, the Chinese government has formulated a series of national goals of total energy control and energy structure optimization. Under the national constraints, how to efficiently allocate the constrained total amount of energy consumption to each province is a fundamental problem to be solved. Based on a data envelopment analysis (DEA) model and a zero-sum game theory (ZSG), this paper constructs a weighted zero-sum game data envelopment analysis (ZSG-DEA) model to allocate the energy consumption quota. Additionally, this paper compares the results with the current administrative targets, to examine the efficiency and feasibility of each allocation mechanism. Finally, this paper employs the proposed model to determine the optimal energy structure for each province in China. The results indicate that by 2020, the national goal of energy structure adjustment will be realized, and energy structure will be diversified in most regions, whereas the coal-dominated status in primary energy consumption will not change. Additionally, the weighted ZSG-DEA model focuses on allocation efficiency while the government considers more regional economic disparity. Therefore, this study suggests a mixture of the two allocation mechanisms in accordance with specific conditions.

Abstract Concentrating solar power (CSP), a promising renewable energy technology, requires better policy support for its initial implementation, which, in turn, necessitates accurate forecasting of its economic potential. This study develops a model based on meteorological data and local policies to calculate the levelized cost of electricity (LCOE) in 31 provincial-level divisions in China. Based on land occupation, concessionary loan, and technology mode as the independent variables, the LCOE is estimated to be $142/MWh to $781/MWh in sites with direct normal irradiance above 1,800 kWh/m2/yr under current local policies and conditions (with and without thermal storage). Thus, this study lays a solid foundation for forecasting power generation and selecting economically feasible sites. It analyzes the CSP learning curve with respect to technologically advanced trends and scale expansion. With the proper optimization of the technology mode, it is reasonable to expect a significant LCOE reduction and grid parity in certain areas. A comparison of the current policies provides a reference for the Chinese government to formulate subsidy policies that would make CSP more competitive.

Abstract Cities consume more than 67% of global primary energy, the production of which results in approximately three-quarters of global CO2 emissions, exacerbating the global warming trend and related extreme weather events and natural disasters. Therefore, it is critical for cities to use existing and new sources of energy efficiently and effectively. This paper introduces a methodology that can combine sustainable energy planning with economic analysis, proposing a form of sustainable urban energy planning that could reduce energy consumption with the minimum economic cost. Taking a postindustrial city (Shenzhen, China) as an example, this paper defines four scenarios by which to analyze future projections of energy generation and consumption from 2015 to 2030 based on the Long-range Energy Alternatives Planning System model. Also developed are Sankey maps for the energy flow from the energy supply to demand sectors for different scenarios. The results show that energy efficiency improvement and energy structure upgrade policies implemented in Shenzhen would have a significant impact on its energy system. Energy consumption is projected to increase steadily up to 2030 under each scenario except for the Peak Scenario, but with different growth rates. Electricity generation in all scenarios is supposed to expand by 2030 and sustainable electricity (such as distributed photovoltaic power, waste-to-energy power, and Combined Cooling, Heating, and Power) will play an important role in the Energy structure upgrade and Peak scenarios.

This paper describes the progress of the research and practice on incorporating mobile sources, especially motor vehicles, into Shenzhen Emissions Trading System. Insights gained through Shenzhen's experience will provide useful insights to others that may have cause to consider the expansion of their Emissions Trading System (ETS) to include mobile sources. In order to incorporate public transportation into the ETS pilot, the city has formulated quantitative greenhouse gas emissions standards for bus and taxi companies, drawn the baselines for energy consumption and carbon emissions of public transportation, and revised local emissions trading regulations. For the further inclusion of non-public transportation vehicles, studies were made on emissions quantification, allowances allocation, non-compliance penalties, and emission reductions certification. In the future, the work will focus on legal safeguards and trading mechanisms.

China has implemented strict policies for the installation of desulfurization facilities in coal power plants in order to mitigate their negative environmental and human health impacts. However, it is rarely acknowledged that desulfurization processes lead to increased water consumption and carbon emissions from the coal power sector. By using a bottom-up approach, we quantified that the desulfurization facilities in all of China’s coal power plants together avoided emissions of 29.52 Mt of SO2 in 2014, with expenses of 550.26 million m3 of increased water consumption, and 53.28 Mt of additional CO2 emissions. Such conflicts were especially pronounced in the North China Grid, where 9.77 Mt of SO2 emission reductions were realized at expenses of 132.15 million m3 of water consumption, and 14.25 Mt of CO2 emissions. The provinces in the North China Grid were already facing extreme water scarcity. Furthermore, while more than 90% of China’s coal power plants have installed desulfurization facilities, the application of full desulfurization would further reduce the greatest amount of SO2 emissions with the smallest amounts of additional water consumption and carbon emissions in the Northwest Grid. Replacing all wet desulfurization facilities with dry ones saves 498.38 million m3 of water consumption in total, and reduces 26.65 Mt of CO2 emissions; however, this is at an expense of 14.33 Mt of SO2 emissions. These conflicts are most pronounced in Shanxi Province in the North Grid, and in Guangdong Province in the South Grid.