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Green power trading has become a concerted global action to address climate change and promote green and low-carbon transformation, and its effect on carbon emission reduction in China's power sector is becoming increasingly pronounced. Exploring the development of China's green power trading market and assessing the impact of green power trading on the electricity market can promote the construction of a new type of power system and help realize the goals of reaching peak carbon emissions and carbon neutrality. The Beijing-Tianjin-Hebei region, one of the important economic hubs and urban agglomerations in China, fully launched green power trading in 2021. The study of green power participation in the electricity market in the Beijing-Tianjin-Hebei region can provide experience and policy references for the development of China's green power market. Therefore, this study analyzed the coupling mechanism between the green power trading market and the electricity market, proposed a non-cooperative game model considering the coupling between the two markets as well as a system dynamics model, and analyzed the impact of green power trading on the behavioral decisionmaking of market players and on the electricity market in the Beijing-Tianjin-Hebei region. The results showed that: 1 The proportion of green power generation affected the behavioral decisions of market players, and increasing the proportion of green power generation increased the profits of green power manufacturers and power grid enterprises and reduced the profits of thermal power plant operators and the cost of power purchases by electricity users. 2 Without considering the scenario of purchasing electricity from outside, feed-in tariffs for green and thermal power were on a downward and then upward trend; the green power installed capacity under construction and generation capacity were increasing, and the green power feed-in was estimated to account for about 63% of the total power in 2030; and the thermal power installed capacity under construction showed a rising, then falling, then rising trend. However, its rate of increase was smaller than that of green power. 3 Considering the scenario of purchasing electricity from outside, the green and thermal feed-in tariffs would decrease; thermal power generation would increase from January 2021 to April 2024, while thermal power generation would decrease from May 2024 to December 2030; green power generation would increase from January 2021 to April 2029 and decrease slightly from May 2029 to December 2030. The sensitivity analysis of purchased power revealed that increasing purchased power had a weakening inhibitory effect on thermal power in the region and promoted green power development. Based on the above conclusions, this study puts forward the following policy recommendations: accelerating the transformation of the power supply structure and promoting the changing roles of green power and thermal power in the power market; adjusting the pricing model of thermal power and promoting the multi-dimensionalization of electricity value; and encouraging inter-provincial and inter-regional green power trading to accelerate the construction of a unified national electricity market. [ABSTRACT FROM AUTHOR]

In order to accelerate the construction of a clean, low-carbon, safe and efficient energy system, China set the provincial weight of responsibility for renewable energy power consumption and established a renewable energy power absorption guarantee mechanism in 2019. As a market incentive policy, it has enduring effect on the low-carbon transformation of the power industry. Firstly, the operation mechanism of the renewable energy consumption guarantee mechanism is analyzed. The general framework, core elements and supporting measures are clarified. Secondly, a stock-flow diagram is constructed based on the system dynamics method. It contains the green electricity market sub-module, the green-certificate market sub-module and the excess power absorption market sub-module. Finally, multiple scenarios are set up to simulate the impact of the green-certificate market and excess power absorption market improvements on the installed capacity and tariff of China's green power market. The renewable energy guarantee mechanism is an effective means to promote the consumption of green electricity in China. In addition, in the short term the cost of electricity for users has increased, but in the long term the cost of electricity shows a fluctuating downward trend. This study provides theoretical references for the formulation of clean and low-carbon policy objectives in the power industry and the optimization of market mechanisms. [ABSTRACT FROM AUTHOR]

The national carbon emission trading mechanism is an important policy tool for the Chinese government to control and reduce greenhouse gas emissions by using the market mechanism. The Beijing–Tianjin–Hebei power market is the focus of energy conservation and consumption reduction in China. Problems have already existed in the synergic development of the Beijing–Tianjin–Hebei power market and carbon trading market. In this article, the development status of the Beijing–Tianjin–Hebei power market is analyzed and the coupling mechanism between the carbon market and power market is combed out to build a synergism model of the carbon market and the Beijing–Tianjin–Hebei power market based on the system dynamics. From the research results, firstly, the Beijing–Tianjin–Hebei power market comes with a high energy consumption intensity and a high proportion of carbon emissions. The coupling of carbon market and power market forces the power industry to reduce carbon emissions through the effective transmission of carbon costs to power prices. Secondly, carbon price shows an upward trend in the context of the current policy scenario, which can give play to the role of price signal in the future. The revenue of thermal power plants, which are the carbon emission right sellers, with new technologies, has increased significantly, while the revenue of carbon emission right buyers, which are the manufacturers of undeveloped units, has increased less. Finally, the technical progress of thermal power plants, the introduction of auction mechanism, the increase in initial carbon price settings and the direct transmission of carbon costs are all factors that promote the effectiveness of carbon trading policy tools in the Beijing–Tianjin–Hebei power market. This study provides theoretical guidance for the synergic development of the "power-carbon" market. [ABSTRACT FROM AUTHOR]

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms' optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings. Fig. 1. Introduction of the research questions Fig. 2. Framework of this paper. [Display omitted] • Carbon emission trading and green certificate trading policies were introduced into a dynamic CGE model. • The comprehensive performance of different scenarios were measured using total factor productivity index. • The policy effects of different scenarios were explored from 2020 to 2060. • The even more effective reform measures were adopt, within a single policy. • A sensible mix were proposed, within different policies. [ABSTRACT FROM AUTHOR]

Increased urban land use efficiency (ULUE) is a pivotal symbol of urbanization's evolution from rapid expansion to a focus on high-quality development. However, the extent to which the spatial-temporal trends in China's future urban sprawl efficiency could hinder the attainment of Sustainable Development Goals (SDGs) 11: "Building Sustainable Cities and Communities" remains uncertain. This study endeavors to shed light on this issue, offering insights that could facilitate the dynamic adjustment of land resource use efficiency assessment strategies to align with the SDG11 urbanization process. We leverage socio-economic shared pathways (SSPs) thematic data through the application of the slack-variable-based data envelopment analysis (SBM-DEA) methodology to project ULUE for China's 368 prefectural-level administrative divisions from 2020 to 2050. In addition, we utilize the coupled coordination degree (CCD) model to anticipate the future harmonization trend of the ULUE with the UN's endorsed SDG indicator, SDG11.3.1: "Land Consumption Rate to Population Growth Rate (LCRPGR)". We harness tools such as Kernel Density Analysis, Moran's Index, and Multi-scale Geographically Weighted Regression (MGWR) to unveil the principal factors influencing the CCD changes and spatial clustering trends. We find that: the higher ULUE areas in China gradually shift from coastal cities and provincial capitals to the western regions, and the value of China's LCRPGR gradually decreases. By 2050, China's ULUE and LCRPGR will reach a high degree of coupling under the SSP2, SSP4, and SSP5 routes. The spatial heterogeneity of the CCD is increasing, with urban land areas having the most substantial adverse effect on CCD, and Gross Domestic Product (GDP), population, and metropolitan green space areas having sequentially decreasing positive effects on CCD. This study assesses the future SDG 11.3.1 process, which helps to adjust the assessment strategy of SDG11 in due time and promote sustainable urban development. [Display omitted] • To predict the ULUE and LCRPGR of prefecture-level divisions in China 2020–2050. • Revealing the coupled coordination between ULUE and LCRPGR in China 2020–2050. • ULUE and LCRPGR reach excellent coordination under the SSP2, SSP4, SSP5 in 2050. • CCD has the strongest negative and positive correlation with land area and GDP. • SDG11.3.1 needs to include economic development and ecological service indicators. [ABSTRACT FROM AUTHOR]

[Display omitted] • To capture the spatial–temporal heterogeneity of urban expansion in China 1995–2020. • The spatial clustering characteristics of Chinese urban built-up areas are revealed. • From 1995 to 2020, size of urban built-up areas in China have increased by 9.1 times. • Urban built-up correlated with the rivers, canals, reservoirs, and agricultural land. • We suggest that changes in watershed should be attended to during urban expansion. Urbanization has accelerated in China, resulting in a noticeable conflict between urban expansion and land use/land cover (LULC). This issue can exacerbate ecological and developmental problems in urban neighborhoods, so it is crucial to explore the relationship between long-term urban expansion and LULC spatial and temporal transitions. We first defined urban built-up areas in terms of human economic activity and then used spatial measures to examine urban expansion and spatio-temporal changes in 25 LULC classes by analyzing 369 cities across China. Our findings indicate that China's urban built-up areas expanded 9.1 times between 1995 and 2020, with the fastest growth rate occurring between 1995 and 2005 at about 16%. We observed a negative association between China's urban built-up area and dry farming land, forests, and grassland in that order, with grassland exhibiting the weakest correlation. The negative correlation between urban built-up areas and paddy fields became positive after 2010, while the positive correlation with reservoirs and canals became negative after 2015. This study utilized spatial measures to demonstrate the spatial heterogeneity of urban expansion in China and explored the extent to which different LULC area changes explained urban expansion. We provide a reference for decision-making efforts on land management and sustainable use in urban areas. [ABSTRACT FROM AUTHOR]

Attentional bias is the interaction that occurs between emotion and attention. Monoamine oxidase and dopamine β-hydroxylase are involved in the balances of neurotransmitters in the cortex. Much evidence has shown that those enzymes play important roles in human emotion and attention. To investigate the potential influences of some functional polymorphisms in DBH, MAOA, and MAOB on attentional bias, we performed a population-based study in a young Chinese Han group. The results indicated that -1021C/T in DBH was associated with index effect of the neutral facial expressions in spatial cueing task (F = 4.940, P = 0.007), and there was a positive correlation between the dosage of C allele and the index effect (r = 0.068, P = 0.040). Furthermore, we found significant interactions between 19-bp Ins/Del in DBH and VNTR of MAOA on attentional biases for negative expressions in spatial cueing task (F = 3.397, P = 0.009) and dot-probe task (F = 2.827, P = 0.024). The present study suggests that DBH and MAOA can influence human attentional biases, and there is a gene-gene interaction between the DBH and MAOA on attentional bias for negative expressions.