The emergence and growth of green bonds is primarily propelled by policy measures. The players in the green bond rating and evaluation market exhibit significant responsiveness and reliance on government stimuli and regulatory sanctions. This paper considers the four-party participants in the green bond rating evaluation supervision system as an interactive whole and develops a four-party evolutionary game model of "double rating + double supervision" under incomplete information based on Peng Ke's (2020) research on the rating supervision model. The model is used to analyze the impact of asymmetric fiscal incentives and regulatory constraints on the quality of green bond rating evaluation information and regulatory effectiveness. Empirical tests are conducted on green bond issuance data from 2016 to 2021 to examine the impact of incentive policies and regulatory constraints on the validity of green bond rating evaluation information. The research findings indicate that the quality of green bond rating evaluation information is influenced by the degree of regulatory penalties imposed by regulators and the Green Bond Committee, direct regulatory costs, and government financial incentives. The strategic sensitivity of green bond rating evaluation agencies to strict regulatory measures is greater than that of government financial incentives.

Accurate carbon accounting is foundational for power enterprises' participation in the carbon market. Current research on estimation of carbon dioxide emissions through electricity‑carbon index analysis primarily relies on an enterprise's total electricity consumption, which often leads to uncertainty and poor interpretability. In reality, carbon dioxide emissions within an enterprise are predominantly generated in specific key processes, indicating a strong correlation between the electricity consumption of key equipment and carbon dioxide emissions. In this context, to enhance both estimation accuracy and interpretability, a two-stage deep learning structure is proposed. This structure leverages non-intrusive load monitoring and employs deep learning algorithms to first disaggregate an enterprise's total electricity consumption into the consumption of key equipment and then use these data to estimate carbon dioxide emissions. Utilizing real-time data from a power plant in China, the proposed two-stage deep learning structure as well as three representative deep learning networks (Recurrent Neural Network, Long Short-Term Memory network, and Gated Recurrent Unit network) are applied to construct electricity‑carbon models for carbon dioxide emission estimation. The experimental results highlight that when employing the two-stage structure, models across three deep learning networks demonstrate a marked enhancement in estimation accuracy compared to traditional models. The two-stage structure reduces the mean squared error (MSE) for models across these three networks by 55.1%, 47.9%, and 46.9%, respectively, compared to their baseline values. This research aims to enhance the precision of carbon dioxide emission estimation and serves as a valuable reference for electricity‑carbon research in various sectors.

Distributed multi-energy systems (DMS) have received increasing attention. Many studies have optimized the capacity and operation strategies of DMS based on multiple objectives, but these studies must discuss the weights of different objectives and have not considered the internal coupling between different objectives. Besides, existing studies have not considered the changes in the actual value of environmental impacts. To address these shortcomings, this paper constructs a technology-economic-environmental optimization model by mixed-integer linear programming. Based on life-cycle assessment, the model quantifies the value of system life-cycle environmental impacts by introducing carbon price. The case results show that the proposed optimization model can reduce the total cost by 28.83% and the life cycle environmental cost by 3.39% compared to the traditional model. To reduce the strain on the grid, a new operation pattern (The grid provides fixed electricity to the system throughout the year.) is proposed. The electricity interaction of the system with the grid under the new operation pattern is more than 70% lower than the system without electricity purchased quantity constraint. Sensitivity analysis shows the total system cost is more sensitive to natural gas and electricity price than carbon price. But carbon price volatility helps reduce system carbon emissions.

China's decarbonization is indispensable for the large-scale utilization of renewable energy. In this process, the development of offshore wind energy has become an important support, because in the eastern region of China, especially the coastal areas, there's the most load requirements but limited onshore renewable resources. Under the goals of both carbon neutral and sustainable development, however, there's still a research gap in evaluating the environmental impacts of large-scale offshore wind plants in China. In this study, the research performed a comprehensive process-based life cycle environmental analysis of a large-scale (400 MW) offshore wind farm with large wind turbine units (5 MW) in China. Global Warming Potential is 25.73 g CO2-eq/kWh and greenhouse gas payback time is calculated as 12.05 months. Fossil fuel consumption amounts to 0.31 MJ/kWh with an energy payback period of 25.56 months. Material resources and ecotoxicity to freshwater need more attention with the values of 1.68 CTUe/kWh and 3.32 10-6 kg Sb-eq/kWh, respectively. The manufacturing stage contributes most to all the impacts examined. The disposal and recycling process shows significant environmental benefits for most impacts but exerts burdens on the particulate matter. Focusing on Global Warming Potential, the wind turbine generation set has the highest effect, among which the blades contribute 73.2% of it. Further, the evolution of electricity and heating mix of China and use of biomass-based precursors could decrease 57% of the blades' greenhouse gas emissions in 2050. This study could provide guidance on the sustainable design and policy-making of offshore wind farms.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) represents a significant economic zone with a diverse financial landscape. Understanding the spatial distribution of financial resources within this area is crucial for promoting balanced economic growth and financial development. This study investigates the spatial patterns of financial agglomeration in the GBA, identifying key influencing factors and assessing their impact on the region’s financial landscape. We employ the entropy value method to evaluate financial agglomeration levels across the GBA’s cities. Additionally, we use spatial econometric techniques to analyze the spatial correlations and the Geo-Detector model to determine the primary factors influencing financial agglomeration. The analysis reveals an overall increase in financial agglomeration, with significant disparities among cities. Key factors driving this agglomeration include transportation infrastructure, overseas trade, foreign direct investment (FDI), and technological advancements. Hong Kong and Shenzhen display notable unevenness in the distribution of financial industries. The interplay between finance, technology, and industrial sectors suggests considerable development potential. Understanding and optimizing the spatial distribution of financial resources is essential for fostering high-quality financial development and sustainable economic growth in the GBA. This study provides insights that can inform policy decisions aimed at enhancing financial integration and cooperation within the region.

Power grids play a crucial role in connecting electricity suppliers and consumers. They facilitate efficient power transmission and energy management, significantly contributing to the transition toward low-carbon practices across both upstream and downstream sectors. Effectively managing carbon reduction in the power industry is essential for enhancing carbon reduction efficiency and achieving dual-carbon goals. Recent studies have focused on the outcomes of carbon reduction efforts rather than the management process. However, when power grids prioritize the process of carbon reduction in their management, they are more likely to achieve better results. To address this gap, we propose an evaluation model for managing carbon reduction activities in power grids, comprising the carbon management efficiency (CME) module based on the maturity model and the carbon reduction efficiency (CRE) module based on the entropy method. The CME module provides a scorecard corresponding to a detailed and continuous evaluation model for carbon management processes to calculate its performance. Simultaneously, the CRE module relates carbon reduction results to the development direction of the government and power grid, allowing for effective adjustments and updates based on actual situations. The evaluation model was applied to provincial power grids within the China Central Power Grid. The results reveal that despite some fluctuations in carbon reduction performance, provincial power grids within the China Central Power Grid have made continuous progress in carbon reduction efforts. According to the synergy model, there is evidence suggesting that power grids are steadily improving their carbon reduction performance, and a more organized approach would lead to a greater degree of synergy. The evaluation model applies to power grids, and its framework can be extended to other industries, providing a theoretical reference for evaluating their carbon reduction efforts.

Based on endogenous growth theory,this article uses data from 31 provinces in China from 2001 to2019 to explore the effect of trade openness and FDI on domestic investment.The study finds that the effect of trade openness and FDI on domestic investment is non-linear in terms of transaction efficiency as a mechanism.Specifically,trade openness has a negative effect on domestic investment under low transaction efficiency.However,it has a promotion effect under moderate transaction efficiency.As transaction efficiency increases,the promotion effect of domestic investment by trade openness would gradually weaken before it disappears;FDI has a negative effect on domestic investment under low transaction efficiency,and a positive effect on domestic investment under high transaction efficiency.Taking industrialization level as a variable to further the research,as China’s industrialization process continues to deepen,industrial structure optimization is also a conditional threshold that affects trade opening and FDI on domestic investment.From the perspective of transaction efficiency,most areas of China should further improve the transaction efficiency so as to consistently release the positive effects of trade openness and FDI on domestic investment.The Midwest should expand its investment in infrastructure,marketization,urbanization,and education to reduce the uneven development of China’s regional economy.

随着人口预期寿命的延长和新生人口比例的减少,我国迈入深度老龄化社会,家庭的养老储备充足性面临挑战.在储蓄养老向财富养老转型的背景下,探究家庭如何合理、有效地配置资产来积极应对老龄化,对银发时代家庭金融风险的防范和化解具有重要意义.本文基于现有文献,梳理了人口老龄化对家庭资产配置的影响:首先根据家庭财务决策的过程,从风险约束、效用函数、信息效率这三个角度重点分析了老龄化如何影响家庭参与风险金融市场的决策;然后从生命周期的角度总结了家庭在不同年龄阶段的投资组合多样性;最后基于风险偏好和信息效率,整理了老龄化对于家庭投资组合有效性的影响.此外,本文还提出了未来可以继续探索的两个潜在研究方向.

Carbon price prediction can help participants keep abreast of carbon market dynamics and develop trading strategies. It is challenging for statistical models to accurately capture the nonlinear characteristics of the carbon pricing, and machine learning methods need sophisticated artificial feature engineering. To successfully address these drawbacks, our research suggests a carbon price forecasting model built on a deep learning architecture. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise decomposes historical price to obtain Intrinsic Mode Function and Principal Component Analysis reduces the dimensionality of each influential factor. Dual-Stage Attention-Based Recurrent Neural Network, a Seq2Seq model, made up of an encoder with feature attention and a decoder with temporal attention, is employed to predicted price of the Hubei Carbon Emissions Allowance. The dual-attention mechanism enables preprocessing to be done adaptively and more effectively than manual processing. As shown by statistical analysis and grey correlation analysis, Hubei Carbon Emissions Allowance has a high autocorrelation, and the carbon market, energy and industry, economy, and environment have high to low correlations on it. The accuracy metrics of this framework, Mean Absolute Error = 0.75, Mean Absolute Percentage Error = 1.59 and Root Mean Squared Error = 1.28, are lower than compared models.

近年来,金融科技公司迅猛发展,在与传统金融机构的合作和竞争中重塑了金融业格局.部分金融科技公司持有多类金融牌照,业务条线涉及不同领域,系统重要性逐步显现.本文主要讨论了巴塞尔监管规则下金融科技公司系统重要性监管的问题,提出应该关注金融科技公司系统重要性评估指标,增强系统重要性金融科技公司的风险防范能力,重视关键业务的恢复和处置计划,提升系统重要性金融科技公司的全面风险管理能力,注重系统重要性金融科技公司监管的适当性.

Consumer finance plays a positive role in boosting consumption and promoting domestic circulation,while the increase in consumption levels and acceptance of lending has laid the foundation for the booming consumer finance industry. The rapid growth of the consumer finance industry and the significant increase in online penetration have also raised widespread concerns about scenario-based consumer defaults,high co-debt risk and improper collections. This paper compares the main differences between consumer finance and traditional bank credit, and explores how to further regulate the development of consumer finance companies from the perspectives of regulatory mechanism construction and regulatory technology application,so as to try to provide ideas for the future regulatory direction of consumer finance.

The distributed energy system (DES) is a promising technology that could enable decarbonization in the building sector. Comprehensive DES system assessment from a holistic perspective is crucial for system design, operation strategy selection, and performance optimization. This paper proposes a techno-economic-environmental integrated assessment model for comprehensive system evaluation. The DES configuration mainly includes a photovoltaic panel, ground source heat pump, gas turbine, absorption heat pump, and thermal storage tank. The system is simulated under three operation strategies with MATLAB/Simulink, which are following thermal load (FTL), following electric load (FEL), and following electric load with thermal storage (FELTS). Entropy-TOPSIS method is used to evaluate the DES's techno-economic-environmental performance under various operation strategies. The results indicate that the DES' primary energy efficiency ratio under the three operation strategies of FTL, FEL and FELTS are 51.49%, 86.78%, and 125.69%, respectively. The dynamic annual values are 1.05×106 CNY, 7.23×105 CNY, and 5.94×105 CNY, respectively. The total greenhouse gas emissions are 36.2 kgCO2eq/(m2∙a), 22.8 kgCO2eq/(m2∙a), and 16.4 kgCO2eq/(m2∙a), respectively. The entropy-TOPSIS analysis results showed that under FELTS operation strategy, DES performs the best; it has the best indicators for technical and environmental evaluation.

人民币国际化现状概述 2009年,中央六部委联合发布《跨境贸易人民币结算试点管理办法》,标志着人民币国际化战略的正式实施,经历十几年的发展,人民币作为国际货币的各项功能都有了巨大的进步. 贸易结算功能:在曲折中不断前进 在贸易结算功能的发展上,人民币经历了迅速上升—回调—再上升的过程,整体上是在曲折中不断前进的.早期,随着主要国际货币的汇率风险上升,人民币的国际化在周边国家迅速推进;但随着2015年之后人民币兑美元的贬值预期出现加上人民币资产的收益率下滑,人民币的贸易结算功能的发展出现了一定的倒退.如今,在人民币跨境支付系统(CIPS)不断推广、中国与周边国家的经贸往来持续发展的情况下,人民币的贸易结算功能又进入了新一轮发展期.

碳资产管理体系的基本框架 碳资产的定义:碳排放权配额+碳信用 全球碳排放权交易体系基本都遵循限额与交易(Cap and Trade)规则,即在履约期初始,政府通常会设定一个地区的排放总量,随后根据控排企业的实际排放情况加以分配,最终分配给企业的就是碳交易中的"通货"一一配额.对实体企业而言,碳配额实质便成为一种特殊的资产——碳资产.在研究中,有学者将碳排放权配额直接等效于碳资产,并研究碳资产与能源商品市场联动、碳资产风险管理等.此外,有学者将碳减排项目也纳入碳资产的概念框架中.

The nexus of energy, water and greenhouse gas plays a vital role in resource management and response to climate change for all nations. Actions in China is decisive. In this study, the environmentally-extended inputoutput model and structural path analysis are combined to analyze the energy-water-greenhouse gas nexus for China 2017. The sectors of Mining and washing of coal, Production and distribution of water, Mining and washing of coal are revealed to show the highest embodied energy consumption intensity, embodied water consumption intensity, and embodied greenhouse gas emission intensity, respectively. Among all 149 sectors, some sectors are identified to be more sensitive than others: Farming, Production and supply of electricity and steam are more energy water-greenhouse gas sensitive, and Mining and washing of coal, Extraction of crude petroleum and natural gas are more energy-greenhouse gas sensitive, and Animal husbandry, Manufacture of pharmaceutical products are more water-greenhouse gas sensitive. Structural path analysis shows that the top 30 supply chain paths contribute to 34.99% of energy consumption, to 39.18% of water consumption, and to 40.18% of greenhouse gas emissions. By studying the main paths, critical sectors of the energy-water-greenhouse gas nexus are further identified as Farming, Mining and washing of coal, Extraction of crude petroleum and natural gas, Production and supply of electricity and steam, Construction of buildings, Civil engineering. Exploring the energy-water-greenhouse gas nexus of 149 sectors could help to lock the key nodes of China's resource management and greenhouse gas emission control in the supply chains.

本文利用网络爬虫工具收集网贷平台微观数据,使用内生转换模型,发现获得银行资金存管会直接影响网贷平台生存状况,在克服内生偏误后,测算得到正向效应为563天.部分可观测Biva-riate Probit 模型和渐进 DID 法证实,这种积极作用同时来自"筛选"和"指导",证实银行在网络借贷中发挥着第三方认证的作用.中介效应模型的结果显示,获得银行资金存管是一个有效的外部声誉信号,并且通过资金利率、资金期限两个中介途径影响网贷平台的生存状态.

文章基于中国2015-2019年1597家制造业上市公司的数据,通过静态面板模型和动态面板模型对政府补助、研发投入对企业创新绩效的影响进行实证分析.结果显示:政府补助和研发投入都显著提升了企业创新绩效;政府补助对企业创新绩效的促进作用大于研发投入,且在国有企业中更明显;政府补助对研发投入和企业创新绩效之间关系的负向调节作用在国有企业中更为显著.

基于结构性去杠杆的视角,本文将2004-2018年沪深A股上市企业与中国各省级行政单位经济高质量发展指标相匹配,实证检验非金融企业杠杆率与经济高质量发展的关系及其背后可能的调节因素.本文研究发现,非金融企业杠杆率与经济高质量发展之间存在倒U型的非线性关系,且存在合理阈值;上市企业的所在区域、所有权性质的差异会明显影响杠杆率合理阈值的分布;进一步研究发现,全要素生产率与投资规模在非金融企业杠杆率与区域经济高质量发展关系之间发挥明显的调节作用.上述结论在替换关键变量、削弱内生性问题后依旧稳健.本文研究结论具有一定的政策启示,为合理安排不同区域、不同所有权性质的企业杠杆率调控政策提供重要参考.

基于利率平价理论,本文考察了人民币美元汇率对特定FOMC公告反应的区制效应及其在岸与离岸市场的异质性.研究发现:特定FOMC公告对人民币美元汇率有明显影响,利率平价效应显著存在;相对在岸市场,离岸汇率受到特定FOMC公告的影响程度较大;在高波动区制内,降息公告对人民币美元汇率的影响有显著日内效应和滞后效应,但FOMC公告的预期效应在高低波动区制内均不明显;同时,两区制间发生概率转移的可能性较低.本文研究结论为分析特定FOMC公告对人民币汇率市场的影响提供了现实依据,有利于中国货币政策在美联储货币政策对人民币汇率市场的溢出效应中发挥前瞻性效果.