This study presents a comprehensive bibliometric analysis of carbon disclosure research, clarifying its knowledge structure, evolutionary trajectory, and emerging research frontiers. Examination of 985 papers from the Web of Science Core Collection (1982-2024) using CiteSpace indicates exponential growth in publications and a paradigm shift within the global research landscape. China has become a leading contributor, joining established nations such as the USA and the UK. The main thematic research clusters identified include: 1) institutional legitimacy and value addition; 2) stakeholder engagement and institutional governance; 3) inter-country comparisons; and 4) policy effectiveness and innovation complementarity. Burst analysis reveals a shift in the research frontier from legitimacy theory to strategic management, with 'green innovation' and 'investment' emerging as recent focal areas. The research paradigm has evolved from a compliance-driven approach to one that integrates disclosure with corporate strategy and sustainable finance. A substantial knowledge gap remains between policy-driven and innovation-driven research, and the growing significance of non-Western institutional contexts is emphasised.
This paper introduces the significance of green and sustainable marketing and the strategies for green marketing. It then indicates that green marketing requires the establishment of a complete public service system, and analysed multiple factors. It also indicates that the development of a green and sustainable marketing public service system should be combined with Internet of Things (IoT) technology, and an automated marketing public service system should be constructed. Subsequently, blockchain technology (BT) is introduced. The paper introduces its core technologies, including the consensus mechanism, encryption algorithm, and smart contract. It analyses their application in the green and sustainable development (SD) of the marketing public service system. In the simulation experiment section, the effectiveness of the green marketing public service platform system based on a blockchain mechanism is tested across three aspects: the transaction volume per second over a given time period, and the numbers of successful and failed system links to customers.
Sewage treatment plants (STP) significantly impact local tourism and ecotourism by improving water quality, enhancing scenic accessibility, and boosting tourist experiences. This study analysed STP effects in Xi'an, comparing suspended solids and tourism revenue in four scenic areas in June 2023 and June 2025. Results showed STP reduced suspended solids, improved environmental quality, and increased tourism revenue. Specifically, scenic areas exhibited notable decreases in water pollutants and substantial increases in income. STP supports ecosystem protection, enhances tourism resources, and promotes sustainable development. This highlights STP's role in balancing environmental protection and tourism growth, offering insights for sustainable ecotourism strategies.
This paper proposes a multidisciplinary optimisation strategy for achieving a 'carbon sink-healthcare-biodiversity' triple win. Integrating data, a collaborative evaluation system is established to assess carbon sinks and ecological services using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. A spatial planning model optimises the layout of forest healthcare bases, balancing carbon sink efficiency with healthcare functions and employing the Nondominated Sorting Genetic Algorithm II (NSGA-II) algorithm and analytic hierarchy process (AHP) for plan selection. Additionally, a community carbon sink return-sharing mechanism is designed, to enhance government goals, enterprise returns, and community satisfaction. The results show that based on the high ecological benefits of 82% government ecological goal achievement rate and 13.4% annual carbon sink growth rate, the enterprise still maintains a 12.1% return rate and 85% community satisfaction. The multidisciplinary approach achieves a 'triple-win' situation of collaborative optimisation of carbon sinks, healthcare, and biodiversity.
To effectively address the environmental challenges confronting rural enterprises, optimising the supply chain while ensuring sustainability is of paramount importance. This study proposes a green supply chain (GSC) optimisation framework specifically designed for rural enterprises to assess and mitigate their environmental impacts. An enhanced Mamba structure is first introduced to identify congestion points within rural supply chains. By integrating these congestion nodes with environmental parameters, a multi-objective optimisation (MOO) strategy is developed to simultaneously reduce pollution and minimise resource loss. This approach facilitates the establishment of a GSC tailored to the distinctive operational characteristics of rural enterprises. Experimental results reveal that the proposed congestion node detection system achieves an F-value of 0.824, while the environmental impact detection accuracy of the optimised supply chain reaches 0.845, indicating a substantial improvement in supply chain performance within rural contexts.
In order to address the difficulty of quantifying the cooling benefits of urban green infrastructure as health benefits and the lack of comprehensive resilience evaluation, this paper develops a systematic evaluation framework that integrates environmental and health data. Firstly, this paper retrieves urban surface temperature from land satellite remote sensing images; secondly, uses the ENVI met model calibrated with local parameters to simulate the cooling intensity and spatial range of different types of green spaces on typical heat wave days; then, combines population distribution with exposure response relationships established based on local epidemiological data, adds the Social Vulnerability Index (SVI); and finally conducts Multi Criteria Decision Analysis (MCDA). The research results indicate that the Gini coefficient of high-risk and highly vulnerable populations decreases from 0.68 to 0.52, and the coverage rate of high-risk communities increases to 78.3%.
Soil microbial environmental quality is an essential indicator of ecosystem health and agricultural productivity. However, current monitoring and management face challenges, including difficulty in integrating multi-source data and the poor sustainability of management measures. To address these issues, this article investigated a comprehensive method based on multimodal learning and graph neural network (GNN). By utilising multimodal learning models, multiple data sources-including microbial sequencing, soil physicochemical properties, and climate data-were integrated, and the features of each modality were extracted and fused. Using a GNN, the complex relationships between microorganisms and environmental factors were modelled to generate reliable predictions of ecological quality. Based on the predicted results, an adaptive management framework was designed to adjust management measures using real-time monitoring data dynamically. Finally, automatic optimisation of management strategies was achieved by applying a dynamic management system.
The widespread use of coal, oil, and natural gas, as well as the massive emissions of greenhouse gases such as carbon dioxide, have caused increasingly serious environmental pollution and ecological damage. This paper focuses on the important global issues of coordinated development of carbon emissions (abbreviated as CE for convenience), energy, and sustainable growth, and improves this field by applying research methods based on fuzzy system theory. This paper adopts a research method based on fuzzy system theory for CE, energy, and sustainable growth, including fuzzy set modelling, fuzzy reasoning and decision-making, system optimisation, and control. In addition, based on fuzzy system theory, this paper analyses the evaluation indicators of CE, energy, and sustainable growth. When the energy consumption is 5000 kilowatt hours, the environmental protection index is 0.3. The success of this research contributes to achieving sustainable and coordinated development of energy and economy in a carbon emitting environment.
The existing bidding strategies for data centres do not fully consider electricity price fluctuations, load regulation capabilities, and carbon emission limitations, making it difficult to strike a balance between the interests of the electricity and rapid frequency regulation markets and low-carbon goals. This paper constructs a two-layer optimisation model based on Stackelberg game theory. The upper layer optimises the bidding price for the data centre, while the lower layer models load regulation, energy storage charging and discharging, and carbon emission constraints using mixed integer linear programming (MILP). Lagrange relaxation method is used for decomposition and solution, while deep Q-network (DQN) algorithm is used for dynamic simulation of market electricity price fluctuations. The experimental results show that after using MILP combined with Lagrange relaxation and DQN optimisation strategies, the market revenue of the data centre increased to 158300 yuan, which is 25.4% higher than traditional methods.
