Amid global environmental enhancement and climate action, coordinating carbon peaking, carbon neutrality, and China’s domestic-international dual circulation strategy drives the nation’s green transition and high-quality economic development (HQED). This study investigates the coupling relationship and underlying mechanisms between pollution reduction-carbon reduction (PCR) and HQED across major in Chinese urban agglomerations. Using panel data from 41 cities during 2010–2020 in thet Yangtze River Delta (YRD), Pearl River Delta (PRD), and Beijing-Tianjin-Hebei (BTH) regions, HQED is quantified via the TOPSIS method, and analytical frameworks include panel quantile regression and fixed effects models. Results indicate that: (1) PCR positively promotes HQED, albeit with diminishing marginal returns; (2) the PRD exhibits significant gains under targeted PCR policies, whereas YRD and BTH show more limited impacts; and (3) key transmission pathways involve energy system transformation, institutional reforms, and green, open, and innovation-driven development. Policy implications suggest tailoring intervention strategies to the developmental stages of urban agglomerations to achieve synergistic advancement of PCR and HQED.

Coordinated speed planning and energy management for intelligent connected hybrid electric mining trucks (ICHEMTs) in car-following scenarios is critical to ensuring transportation safety and achieving low-carbon, energy-efficient operations. However, random disturbances from the preceding vehicle cause unnecessary host vehicle speed fluctuations. Furthermore, dynamic loads interacting with undulating terrain increase operational uncertainty, resulting in highly nonlinear, time-varying energy consumption and safety boundaries. Thus, it remains a pressing challenge to achieve coordinated optimization of car-following speed and energy management, particularly under the coupled effects of dynamic loads and undulating terrain, while simultaneously ensuring driving safety. To address this, this article proposes a coordinated optimization control method for energy efficiency and driving safety based on the deep deterministic policy gradient (DDPG) algorithm. By incorporating the preceding vehicle's motion trend into dynamic optimization, the method effectively suppresses unnecessary host vehicle speed fluctuations, thereby further improving its energy efficiency. Simulations on real-world mining conditions demonstrate that the proposed method improves economic performance by 7.1% compared to the model predictive controller (MPC)-deep Q-network (DQN) hierarchical optimization approach while ensuring car-following safety. These results highlight its feasibility for balancing transportation safety and energy consumption control under complex conditions, supporting energy-saving and emission-reduction goals in green mine development.

Land-sea coordination level is one of the most important benchmarks for evaluating the high-quality economic development of coastal regions. To comprehensively and objectively assess the land-sea coordination level in the six coastal cities of Liaoning Province, this paper constructs a measurement index system from four dimensions: resources, environment, economy, and society. After standardizing the data, the entropy weight method is employed to calculate the weights and comprehensive values, and the coupling coordination degree model is utilized to measure the land-sea coordination level in each city. Combining the obtained coupling coordination degree and comprehensive values, this study classifies the development level of land-sea coordination and analyzes the spatial differentiation characteristics of the coastal cities in Liaoning Province. The results indicate that Dalian belongs to well-coordinated cities, while the other five cities are classified as barely coordinated. Among them, Dalian, Yingkou, and Panjin are ocean-dominated cities, Jinzhou and Dandong are land-dominated cities, and Huludao exhibits simultaneous land-sea development. Finally, the paper analyzes the reasons for spatial differentiation based on the specific conditions of each city and discusses strategies to develop land-sea coordination tailored to local conditions and promote regional coordination.

Based on the panel data of the regional GDP balance of pollutant and carbon dioxide emissions in 21 cities of Guangdong Province from 2006 to 2020, the dynamic equilibrium relationship between pollution reduction and carbon reduction and the economy was investigated using a PVAR model. The results were as follows： ① A self-growth mechanism of inertia effect exists in the Guangdong economy compared with pollution reduction and carbon reduction. ② The causal relationship between the economy and pollution control was mainly reflected in the inhibition effect of pollution discharge on the economy. The response of water pollutant discharge to economic shock changed from positive to negative, and the response curve of air pollutants to economic shock was V-shaped. The economy had a promoting effect on pollutants. ③ No two-way causal relationship exists between carbon emissions and economy, and the positive weakening impact of the economy on carbon emissions lagged behind, which accords with the Kuznets curve hypothesis. ④ A complex and subtle relationship exists between the economy and the synergy of pollution reduction and carbon reduction. The economy had a positive promoting effect on the synergy of pollution reduction and carbon reduction, while the backwardness of the synergy of pollution reduction and carbon reduction inhibited the economy to a certain extent. To achieve the "win-win" situation of high economy and ecological protection in Guangdong Province, we must give full play to the "double dividend" effect of the economy driving the synergy of pollution reduction and carbon reduction, promote the optimization of industrial structure, increase investment in scientific and technological innovation, consolidate the foundation of emission reduction and carbon reduction, and thoroughly implement the strategic deployment of carbon emission peaking.

New quality productivity (NQP) has infused fresh impetus into the construction and development of the modern marine industrial system (MMI), becoming a pivotal force in driving the high-quality development of the marine economy. This paper elucidates the value implications of NQP in fostering the formation and expansion of the MMI. By clarifying the internal mechanisms of this system, we delineate the goal orientation of the MMI enabled by NQP. We conduct an in-depth exploration of the enabling process of the MMI across five dimensions: innovation-driven growth, factor matching, integrated development, supply-demand adaptation, and green and low-carbon practices. The paper highlights that leveraging NQP can unlock the potential of traditional industries, strategically position emerging and future industries, enhance the impetus for innovation in marine science and technology, optimize the environment for marine industry development, accelerate industrial and digital integration, and improve policies related to green industries, technologies, and sustainable development. These measures aim to further implement the maritime power strategy and promote the empowerment and enhancement of the high-quality development of the marine economy.

Cities encounter increasing risks and challenges under new development patterns, and digital economy (DE) can drive cities’ improved resilience to natural and social uncertainties. Based on data collected on the Yangtze River Economic Belt from 2011 to 2021, the development situation of the regional DE and urban resilience (UR) was measured, and the impact mechanisms of the DE on UR were analysed theoretically and empirically using a benchmark regression model. The findings are as follows: The benchmark test revealed that the DE development contributed significantly to UR in the region. The mediating effect test revealed that the DE improved UR through two pathways: industrial structure upgradation and energy restructuring. The moderating effect test suggested that environmental pollutant emissions had a negative moderating effect between the impact mechanisms of the DE, industrial structure, and energy structure and between the effects of industrial structure, energy structure, and UR, whereas CO2 emissions had a positive moderating effect between the impact mechanisms of DE and industrial structure. Heterogeneity analysis indicated that the DE contributed positively to the UR except in the upstream region, where the coefficient of the DE was insignificant. Overall, this study highlights the crucial role of the DE in improving UR and elucidates the theoretical impact mechanisms of environmental pollution control and carbon reduction measures on UR.

Exploring the development laws of new quality productivity and leveraging its role in empowering marine economy high-quality development (MEHQ) is of great significance for promoting the construction of a maritime power in China, achieving the goals of Chinese-style modernization and solidifying the country’s advantages in marine economic development. This paper systematically reviews the mechanisms and effects of new quality productivity (NQP) in empowering MEHQ. Using panel data from 17 cities along the Bohai Sea from 2010 to 2022, we comprehensively measure the combined levels of NQP and MEHQ. Employing various statistical analysis methods, including benchmark regression models, multiple mediation effect models, and spatial Durbin models, we empirically test the mechanisms and spatial spillover effects of NQP in empowering MEHQ. The results indicate that NQP has a significant positive driving effect on MEHQ, and this conclusion remains valid after a series of robustness tests. The empowering effect of NQP on MEHQ mainly occurs through three pathways: marine technological innovation, optimization of marine industrial structure, and improvement of marine resource allocation efficiency. There is a positive correlation between NQP and MEHQ, with significant agglomeration phenomena, reflecting the non-uniform characteristics of spatial distribution. NQP not only empowers MEHQ but also exhibits significant spatial spillover effects, promoting MEHQ in adjacent regions and releasing growth dividends.

While green finance (GF) is widely acknowledged as an instrument for pollution and carbon reduction, its heterogeneous effects on innovation and spatial variations remain underexplored. Using a novel city-level dataset (2010-2021) and a multi-method analytical framework, this study reveals three key findings: First, GF development significantly enhances the synergistic reduction of both environmental pollutants and carbon emissions (PRCR), a result robust to various tests. Second, GF fosters higher levels of technological innovation, which acts as a positive mediator in the relationship between GF and pollution-carbon mitigation. Third, GF primarily drives emission reductions through high-quality green patents in developed regions, while triggering low-quality "compliance innovation" in less-developed areas-a divergence overlooked in previous aggregate analyses. Moreover, GF policies in eastern China generate positive spillover effects, contributing to a 0.18 % reduction in CO2 emissions for every 1 % increase in GF in neighboring regions. In contrast, western cities experience negative spillovers due to industrial relocation, challenging conventional Environmental Kuznets Curve (EKC) narratives. As China is currently undergoing a critical period of rapid GF expansion and energy transition, this study sheds new light on the synergistic mechanisms between GF, pollution abatement, and carbon reduction at the city level. It systematically outlines pathways for synergistic governance and provides empirical insights for integrating GF with ecological civilization construction and urban carbon mitigation efforts.

Achieving robust and energy-efficient navigation in unknown fluid environments remains a key challenge for bioinspired underwater robots. In this study, we develop a reinforcement learning-based control framework that enables a fish-like swimmer to autonomously acquire effective navigation strategies within a high-fidelity computational fluid dynamics environment. By shaping the reward function to favor energy efficiency, the agent spontaneously discovers different locomotion patterns, ranging from continuous bursting to burst-and-coast gaits, all without prior knowledge of fluid mechanics. Although the agent is trained in a quiescent fluid environment, the learned swimming policies are generalized well in various navigation tasks and remain robust under complex flow perturbations, including uniform currents and unsteady vortex wakes. In all test scenarios, the agent achieves a 100%navigation success rate. These findings highlight the potential of integrating physics-based simulation with learning-based control strategy to advance the design of adaptive, efficient, and resilient aquatic robots inspired by biological swimmers.

Most reinforcement learning (RL) approaches for the decision-making of autonomous driving consider safety as a reward instead of a cost, which makes it hard to balance the tradeoff between safety and other objectives. Human risk preference has also rarely been incorporated, and the trained policy might be either conservative or aggressive for users. To this end, this study proposes a human-aligned safe RL approach for autonomous merging, in which the high-level decision problem is formulated as a constrained Markov decision process (CMDP) that incorporates users' risk preference into the safety constraints, followed by a model predictive control (MPC)-based low-level control. The safety level of RL policy can be adjusted by computing cost limits of CMDP's constraints based on risk preferences and traffic density using a fuzzy control method. To filter out unsafe or invalid actions, we design an action shielding mechanism that pre-executes RL actions using an MPC method and performs collision checks with surrounding agents. We also provide theoretical proof to validate the effectiveness of the shielding mechanism in enhancing RL's safety and sample efficiency. Simulation experiments in multiple levels of traffic densities show that our method can significantly reduce safety violations without sacrificing traffic efficiency. Furthermore, due to the use of risk preference-aware constraints in CMDP and action shielding, we can not only adjust the safety level of the final policy but also reduce safety violations during the training stage, proving a promising solution for online learning in real-world environments.

As a national strategic development area, the Yellow River Basin (YRB) has seen progress in research on the synergy efficiency of pollution reduction and carbon reduction (SEPCR). However, there are still notable gaps. The theoretical framework for this area is lacking, leading to diverse and inconsistent conclusions. Additionally, difficulties in data collection and processing, along with incomplete and inconsistent data, negatively affect the accuracy of research findings. Current studies tend to focus on single aspects and lack a comprehensive and systematic analysis of the SEPCR across the entire basin. There is insufficient understanding of key network nodes, connections, and overall structural characteristics. A scientific assessment of its spatial correlation structure has far-reaching implications for the national battle against pollution and the realization of “dual carbon” goals. This study is based on panel data from 75 cities in the YRB from 2006 to 2022. It employs an ultra-efficiency SBM model to measure the SEPCR. Additionally, it utilizes a modified gravity model and social network analysis to explore the spatial network correlation structure in depth. Furthermore, the QAP model is used to clarify the mechanisms of various influencing factors. The research findings indicate that there is an imbalance in the spatial and temporal distribution of the SEPCR in the YRB. Although there is a fluctuating upward trend over time, significant internal spatial disparities exist. While the gaps between regions are gradually narrowing, there are still evident research disparities. Moreover, the spatial connectivity of the SEPCR in the YRB is gradually strengthening, with overall network connectivity also improving, yet there remains a considerable distance from an ideal state. The network density shows a decreasing trend from the downstream to the midstream and then to the upstream regions, with significant differences in spatial network centrality among these areas, particularly pronounced between the midstream and upstream regions. Differences in economic development levels, technological development levels, and industrial structure development levels promote the formation of spatial correlations in SEPCR, while disparities in energy utilization have a suppressive effect.

Against the backdrop of low-carbon development, improving marine fishery eco-efficiency constitutes a core pathway for achieving sustainable industrial development. Based on panel data from 11 coastal provinces (autonomous regions and municipalities directly under the central government) of China during 2010—2022, this study constructed a Super-SBM evaluation model incorporating undesirable outputs such as carbon emissions and environmental pollution to measure marine fishery eco-efficiency. Combined with methods including kernel density estimation and spatial visualization, the spatiotemporal evolution characteristics were analyzed, and the key influencing factors were revealed through the Panel Vector Autoregression (PVAR) model and variance decomposition. The results indicate that the marine fishery eco-efficiency values ranged from 0.370 to 1.300 during 2010—2022, showing an overall three-stage characteristic of “rapid improvement-slight decline-basic stability” and stabilizing at a high level of 0.933～0.949 after 2016. Significant regional differences were observed: the Northern Marine Economic Circle experienced rapid growth followed by stable fluctuations after 2010—2014; the Eastern Marine Economic Circle maintained a consistently high and stable level with slight growth; the Southern Marine Economic Circle showed fluctuating growth but with significant heterogeneity among internal provinces. The spatial pattern evolved from concentration to equilibrium, with overall convergence of regional differences, the standard deviation and coefficient of variation decreased by 24.1% and 31.6% respectively, compared with 2010. Among the influencing factors, the scale effect served as the core driver, with its contribution degree increasing from 0.658 to 0.860 from the 1st to the 5th period; the technical effect had a weak initial contribution but exhibited steady long-term growth to 0.128; The structural effect had a weak and fluctuating contribution with remaining room for optimization. The research findings provide a scientific basis for formulating differentiated policies to promote low-carbon development and enhance eco-efficiency in marine fisheries.

Accurately understanding the evolutionary characteristics and developing trend of carbon emission efficiency of coastal tourism is essential for advancing the green and low-carbon development of coastal tourism and achieving the"double carbon"goal.The"bottom-up"approach was used to determine the total carbon emissions of coastal tourism in 11 coastal provinces and cities between 2011 and 2021.The carbon emission efficiency of coastal tourism was measured using the super-SBM model,and the spatiotemporal evolution of coastal tourism was described using the ML index,tradition,and spatial Markov chain.Finally,a Markov limit distribution matrix was constructed to forecast the trajectory of carbon emission efficiency of coastal tourism going forward.The findings demonstrated that:① The carbon emission efficiency of coastal tourism had a development trend that fluctuated and increased between 2011 and 2021.Except for that during 2019-2020,the ML index was greater than 1,suggesting that the carbon emission efficiency of the coastal tourism sector was growing at a healthy rate.The regional distribution of carbon emission efficiency in seaside tourism varied significantly.② The efficiency transfer of coastal tourism was a long-term and ongoing process,with a phenomenon known as"club convergence"in its carbon emission efficiency.The probability of maintaining the same efficiency was at a minimum of 53.57%.③ The transmission of carbon emission efficiency types in coastal tourism was significantly influenced by the geographical patterns,and the phenomenon of"club convergence"remained present when considering the geographical pattern.④ In terms of the trend prediction of spatiotemporal evolution,the carbon emission efficiency of coastal tourism will gradually shift from a low-level state to a high-level state over time,and the geospatial pattern will significantly affect the trend of the carbon emission efficiency evolution in coastal tourism.

As an efficient long-term carbon sink, marine carbon sinks and the associated carbon sink effects, technology, accounting and trading market construction warrant investigation across various disciplines. However, information on the interrelationships and their development over time with respect to the research conducted in China is limited, affecting the ability to drive research directions and optimize continued advancement in this field. Therefore, in this study, we aimed to understand the current situation of marine carbon sink research in China to promote a deeper level of scientific development based on the research literature related to marine blue carbon sinks in the core databases of the China National Knowledge Internet (CNKI) and Web of Science (WOS). We used bibliometric tools in the Citespace software to quantitatively compare and analyse the main characteristics of marine blue carbon sink research including publication volume, time, journals, authors and institutions. We also explored the popular research topics, frontier areas, and theme evolution trends through keyword clustering and emergent and co-occurring knowledge maps. The key recommended research directions for ocean carbon sinks are: (1) to promote the unified carbon sink market research of land and sea integration through multidisciplinary and cross-disciplinary research; (2) to achieve new breakthroughs in ocean carbon sinks with the support of coastal wetlands and seawater offshore aquaculture environments; (3) to explore the protection provided by ocean carbon sinks with a comprehensive eco-compensation mechanism; (4) to improve the application of marine carbon sinks by taking the theory and technological innovation research related to marine carbon sinks as the guide. Ultimately, our work helps characterise the current situation of marine carbon sink research, promote the research in this field to a deeper level of development and provide reference for subsequent scholars to carry out related research.

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently. This paper proposes a reinforcement learning(RL) method for autonomous vehicles to navigate unsignalized intersections safely and efficiently. The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning. A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them. A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections. The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem. The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency. Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions. The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

The equalization of marine public services is an effective way to achieve harmonious coexistence of the sea. In this paper, a variable fuzzy recognition model is used to measure the equalization level of marine public services in 11 provinces and cities in China’s coastal areas from 2006 to 2019. The Dagum Gini coefficient, kernel density estimation model, and convergence model are used to study their regional differences, distribution dynamics, and convergence characteristics. The results show that the equalization level of marine public services in China’s coastal areas increased year by year from 2006 to 2019. In terms of spatial distribution, the equalization level of marine public services in coastal areas presents an unbalanced distribution pattern. The overall regional differences in the equalization level of marine public services in China’s coastal areas have narrowed, and the inter-regional differences are the main reasons for the overall differences. The absolute difference in the equalization level of marine public services in China’s coastal areas shows an expanding trend. The equalization level of marine public services in China’s coastal areas has α convergence and β convergence.

Research on the marine science and technology innovation efficiency (MSTE) from the perspective of innovation value chain is not only an inevitable requirement for in-depth exploration of Marine science and technology innovation activities, but also an important guidance for the sustainable development and optimization of marine economy. Based on the innovation value chain perspective, the marine science and technology innovation process is divided into three phases: basic innovation, applied research and development, and gainful transformation, and the chain network DEA model is used to measure the MSTE of 11 provinces and municipalities along the coast of China from 2007 to 2021; the modified gravity model and social network analysis are used to examine the spatial correlation network characteristics of the marine MSTE at different phases and their influencing factors. The results show that the spatial correlation of China marine MSTE gradually develops from a sparse and dispersed state to a close trend, and the three phases gradually show a development pattern from unicentre, polycentre and networked. There is no strict hierarchical structure in the spatial correlation network of marine MSTE, the applied research and development and revenue transformation phases are more relevant than the basic innovation phase, and the cross-regional collaborative innovation needs to be improved. The high-efficiency provinces have a strong ability to radiate the MSTE to other provinces, and can absorb a large amount of innovation resources. The spatial correlation network of MSTE development has formed four plates of two-way spillover, broker, net spillover and net benefit in all three phases, and the transmission of kinetic energy of regional MSTE development has obvious gradient characteristics. The strength of government support, marine industry structure, and marine management services are conducive to enhancing the spatial correlation of the three phases of innovation development. Through this study, we can not only grasp the overall pattern and development dynamics of China Marine science and technology innovation, but also deeply analyze the internal logic and formation mechanism of its spatial correlation network structure, so as to provide scientific basis for optimizing resource allocation and improving innovation efficiency.

In the wake of the COVID19 pandemic, what is crucial for the continued promotion of ecological civilization construction and sustainable green development in China is to achieve the dynamic and balanced development between pollution reduction, carbon emission reduction, and economic growth. Therefore, the indexes of CO2 2 emissions, pollution reduction intensity, and high-quality economic development are quantified in this study by using the panel data related to 26 cities in the Yangtze River Delta urban agglomeration(YRDUA) from 2010 to 2020. Furthermore, kernel density analysis, GIS spatial analysis, and other methods are employed to demonstrate the spatial and temporal patterns of pollution reduction intensity, CO2 2 emission levels, and the characteristics of high-quality economic development in the YRDUA. In order to reveal the dynamic relationship between pollution reduction, carbon emission reduction, and high-quality economic development, a panel vector autoregressive model (PAVR) was established and the system generalized method of moments was used to estimate the model coefficients. Additionally, Granger causality tests, impulse response analysis, and variance decomposition were conducted. There is a fluctuating upward trend shown by the overall level of CO2 2 emissions in the YRDUA, with significant spatial nonuniformity exhibited in carbon emissions. Despite the self-promotion mechanism playing a more significant role in the high-quality economic development in the YRDUA and its three regions, the impact of pollutant and carbon emissions on high-quality economic development is quite limited. A significant promoting effect is exhibited in those high and moderately high-quality economic development regions, while nonlinear effects are observed in those high and moderately high-quality economic development regions. As for the interactive relationships, the three factors are correlated with each other to a more significant extent in those high and moderately high-quality economic development regions, which contributes to mutual influence and causal relationship. However, there is no effective interactive mechanism observed among the three factors in the whole area and low-quality economic development regions, with high-quality economic development playing a major role in environmental pollution and carbon emission reduction.

Marine science technology innovation provides power and guarantees for marine eco-civilization construction, which provides direction and material support for marine science technology innovation. Therefore, the coordinated development of the two is of great significance to the marine economy sustainable development in China’s coastal areas. On the basis of clarifying the connotations of marine science technology innovation and marine eco-civilization in China’s coastal areas from 2006 to 2019, the mechanism for their coordinated development was analysed. A comprehensive indicator system based on the connotation of the two was constructed, and the coordinated development relationship was empirically tested using the coupled coordination model and the panel vector autoregressive (PVAR) model. The results show that: 1) the level of China’s coastal marine science technology innovation continues to improve, gradually forming the core of the development of marine science technology innovation in the north, east and south of Shandong, Shanghai and Guangdong; the level of marine eco-civilization development fluctuating upward trend, showing obvious spatial differentiation characteristics. 2) The degree of coordination of marine science technology innovation and marine eco-civilization is growing over time. There is no causal relationship between marine science technology innovation and marine eco-civilization in the northern marine economic circle, but there is a two-way causal relationship between the two in the eastern and southern marine economic circles. 3) Marine eco-civilization shows a significant positive and continuous impact on marine science technology innovation, and marine science technology innovation shows a long-term, continuous, fluctuating, and lagging impact on marine eco-civilization. The overall role of marine eco-civilization on marine science technology innovation is dominant, and there are significant differences in the impact effects of the two major marine economic circles.

Scientific forecasting of carbon emission trends is an important basis for understanding carbon emission levels and a key reference for achieving dual carbon goals. In this study, a carbon emission forecasting model was built based on the gated recurrent unit model to empirically evaluate the data of 16 coastal cities in China from 1997 to 2019. Eight scenarios were established, forecasting carbon emissions in Chinese coastal cities from 2020 to 2029 to investigate the combined effects of the factors driving carbon emissions, including the economy size, industrial structure, and public revenue. Our results predict increasing carbon emissions from 2020–2029, with a significantly slower growth compared with the change in carbon emissions from 1997–2019. Furthermore, the eight scenarios show that changes in driving factors, including public revenue, industrial structure, and the size of the economy, greatly impact carbon emissions. The size of the economy most significantly affects carbon reduction. Therefore, the marine economic growth model must be redesigned, and further developed towards carbon emission reduction, aiming at establishing a virtuous cycle of low-carbon development.