Search Results (1,502)

The inclusion of the cement industry into China’s national carbon emissions trading system in 2025 has fundamentally altered the compliance environment for high-emission enterprises, transforming carbon allowances from passive regulatory instruments into dynamic assets whose management directly affects financial performance. We develop a multi-scenario carbon asset management decision model tailored to the intensity-based benchmarking mechanism adopted by the national market. The model centres on the quota surplus-deficit variable EA4, which is computed from enterprise-level emission intensity relative to the industry benchmark, and decomposes the management problem into sequential selling and buying subproblems linked by coupled decision boundaries. A systematic parameter framework is constructed, and the model is applied to two cement enterprises—Enterprise A, a leading producer with a clear allowance surplus, and Enterprise B, a mid-tier producer operating near the benchmark boundary—through historical backtesting over the 2024–2025 period. Three principal findings emerge. First, the intensity benchmarking mechanism creates a dual-leverage effect whereby a 1.4% improvement in emission intensity (from 0.8112 to 0.8000 t/t) increases the quota surplus by 27%, a nonlinearity not captured by conventional compliance-cost models. Second, the model-driven strategy outperforms traditional experience-based approaches by 36.8% (baseline scenario, +95.20 vs. +69.58 MRMB) and 37.3% (risk scenario, −44.55 vs. −71.08 MRMB), with the improvement rate remaining consistent across both enterprises, suggesting that trading timing outweighs instrument selection in determining compliance cost outcomes. Third, dynamic CEA–CCER allocation captures an incremental 2.33 MRMB through the exploitation of a transient price inversion, a gain invisible to single-instrument strategies. Sensitivity analysis confirms that the relative advantage is robust to carbon price variations (±30%) and CCER offset caps (2–10%), while emission intensity and carry-over allowances represent the most consequential parameters for strategy direction, with EA4 crossing zero near the industry benchmark (I ≈ 0.85). The framework provides actionable decision support for cement and other high-emission enterprises navigating the unified carbon market, and contributes a quantitative methodology to the emerging field of environmental management accounting. This study contributes to Sustainable Development Goal 13 (Climate Action), Goal 7 (Affordable and Clean Energy), and Goal 9 (Industry, Innovation, and Infrastructure) by providing operational tools for decarbonisation in carbon-intensive industries. Full article

To investigate the interaction between artificial intelligence development (AID) and water–energy–food system technical efficiency (WEF-TE), panel data from 264 cities in China from 2013 to 2023 were utilized, and WEF-TE in the study areas was estimated using Stochastic Frontier Analysis (SFA). Subsequently, the Error Correction Model (ECM) and a random forest model were adopted for empirically examining the adjustment and driving mechanisms of AID on WEF-TE from three dimensions, namely enterprise scale, application level, and workforce literacy. The results indicate the following: (1) China’s WEF-TE generally shows an increasing trend; however, clear differences remain between high-value and low-value regions, and the deviation in lagging areas can reach 0.507. Meanwhile, the Yellow River Basin, which is the core region of China’s WEF system, remains below the national average in the process of technical efficiency optimization. (2) AID has a long-term equilibrium relationship with WEF-TE across the research dimensions and can effectively adjust technological inefficiencies in the short term, with adjustment coefficients ranging from 0.004 to 0.021 under different test rules. (3) In terms of enterprise scale and application level, the driving effect of AID on WEF-TE is relatively strong, with feature weights of 0.16 and 0.155, which are close to those of human capital input (0.172) and industrial structure rationalization (0.15). This study provides important reference value for constructing an interdisciplinary research framework that integrates WEF Nexus with AID. Full article

Climate change is a global pressing concern that has affected all sectors, including the operations of Small and Medium Entreprises (SMEs) in developing countries, including Malawi. This has negatively affected their economies of scale and exacerbated the SMEs’ growth constraints. Nonetheless, renewable efficient energy (REE) systems, including solar and biogas, could help in building resilience to sustain their performance. In line with this, the study examined the factors that enhance the adoption of renewable efficient energies and constructed their resilience indices. Our study was grounded in the Diffusion of Innovation Theory and the Sustainable Livelihoods Framework. These theories contextualised the study and guided the selection of variables to estimate an Endogenous Switching Regression (ESR) econometric model, alongside estimating the absorptive, adaptive and transformative individual indices for 699 SMEs, using the 2019 Malawi Household Integrated Survey data. The results initially suggests that factors such as access to credit, being male, access to education, access to capital sources, a large profit share, bridging social capital and location among others, have a positive effect in influencing the adoption of renewable efficient systems. We simulated the adoption results and found that SMEs that adopts REE increase their resilience with an Average Treatment Effect of 0.117 and through the subsidy policy effect vulnerable SMEs that later adopt REE would shift their resilience by 0.169. Furthermore, the study found that transformative capacity plays the most important role in building long-term resilience for the SMEs. The study calls for policies, including establishing urban centres where SMEs can access information regarding REE and improving access to formal safety nets and capital sources beyond loan provisions. Full article

In the digital era, enterprises face increasing pressure to align strategic objectives with operational execution under volatile and data-intensive conditions. Traditional management control systems often rely on lagging indicators and ad hoc interventions, limiting both performance visibility and sustainability outcomes. This study develops MOD-FCA, a prescriptive, multi-layered closed-loop management control framework that links value-centric outcomes to business-centric drivers through vertically aligned metrics, objective tensors, and tiered corrective routines. Using a longitudinal case study in a manufacturing enterprise, we illustrate how MOD-FCA enhances operational traceability, supports systematic deviation identification and response, and institutionalizes organizational knowledge for continuous improvement. Importantly, MOD-FCA is designed to support sustainable industrial practices by embedding sustainability-related metrics, such as resource efficiency, energy intensity, waste reduction, and process compliance, into the same metric deployment, deviation triggering, and corrective-action logic used for operational control. Qualitative feedback from managerial and operational roles indicates that MOD-FCA strengthens accountability, ensures role-aligned responses, and fosters proactive, data-driven decision-making. These findings provide both theoretical contributions to management control system design and practical guidance for enterprises seeking to achieve both operational excellence and long-term sustainability. Full article

Industrial settlements of the East Kazakhstan Region face a persistent technogenic burden driven by the dense concentration of non-ferrous metallurgy and heat-and-power enterprises, further compounded by unfavorable pollutant dispersion conditions inherent to the region’s mountain–basin topography. This study evaluated spatiotemporal associations between annual mean concentrations of NO₂, SO₂, H₂S, and CO, the integrated air pollution index (API₅), and primary neoplasm morbidity across five settlements over the period 2014–2024. A retrospective ecological analysis was carried out for Ust-Kamenogorsk, Ridder, Altai, Shemonaikha, and the settlement of Glubokoe, incorporating Spearman’s rank correlation, lag analysis (1–3 years), and the Mann–Kendall trend test. Throughout the study period, neoplasm morbidity in the region consistently exceeded the national average by a factor of 1.3 to 2.0. In Ust-Kamenogorsk—where metallurgical SO₂ and NO₂ emissions are most heavily concentrated—strong positive associations were found in children for SO₂ (ρ = 0.791, p < 0.05) and in adolescents for NO₂ and CO, reflecting elevated inhalation exposure under conditions of chronic pollution. The negative associations with API₅ observed in Ridder and Altai, where the index showed a statistically significant downward trend, are interpreted as evidence of the inertial character of oncological processes and the lasting influence of cumulative past exposure. Across all studied settlements, SO₂ emerged as the most consistent predictor of morbidity variation. These findings support prioritizing stricter emission controls for SO₂ and NO₂ from metallurgical and energy facilities, establishing oncological screening programs for children and adolescents in chronically polluted areas, and strengthening ambient air monitoring—measures whose effective implementation will require coordinated action between public health authorities and environmental regulators. Full article

Small rural businesses face significant challenges due to geographical constraints, transportation costs, small market size, and low population density. On top of that, the energy crisis that arose after the start of the 2022 Russia–Ukraine war and the sanctions imposed by the EU and the US have created a stifling energy environment. The latter has exposed the businesses to the risk of energy poverty. The current study examines energy poverty within three business sectors that are prominent in the Greek countryside. These are entities firstly involved in the processing, manufacturing, and standardization of agricultural products; secondly, involved in the trade of agricultural products; and lastly, certain businesses operating in the tourist area. More specifically, this research examines the energy needs and energy obligations of these businesses as well as the energy efficiency of their facilities by simultaneously exploring the impact of European and national energy policies on addressing energy poverty in rural micro-businesses. To detect the opinions, experiences, perceptions, estimations, and expectations of entrepreneurs who maintain these businesses in rural areas, a qualitative approach was adopted utilizing personal in-depth interviews. Overall, fifteen micro-entrepreneurs were interviewed. Findings revealed that energy costs for rural businesses are becoming a major issue for their survival. Moreover, they have a substantial effect on their operational costs, exceeding other expenses and leading to an increase in energy poverty. These findings have also been confirmed by statistical data. Energy costs for small businesses range from 15% to 35% depending on the business, and during peak periods or crises, they exceed 40%. In addition, fees and taxes account for over 40% of electricity bills. Full article

Against the backdrop of frequent adjustments and iterations in global climate policies, the issue of policy uncertainty surrounding corporate energy structure upgrades has become increasingly prominent. A key concern for achieving global green sustainable development is how to efficiently advance corporate low-carbon transition. In view of this, we construct the energy structure low-carbon transition at the enterprise level, and explore the influence and mechanism of climate policy uncertainty on the energy structure low-carbon transition of enterprises from the perspective of enterprise willingness and ability. The research findings indicate: (1) Corporate energy structure low-carbon transition is substantially impeded by climate policy uncertainty, and this conclusion is upheld by a battery of robustness and endogeneity analyses. (2) Climate policy uncertainty inhibits corporate energy structure low-carbon transition by reducing management’s long-term behavior, lowering green technology innovation levels, and weakening effective investment. (3) According to heterogeneity analysis, non-state-owned businesses, areas with lax environmental regulations, and businesses with poor climate risk awareness are more affected by the inhibiting impact caused by climate policy uncertainty. In addition to offering theoretical underpinnings and helpful advice for governments looking to create stable climate policies and enhance climate governance systems, this paper gives fresh perspectives on the fundamental reasoning behind corporate energy structure decarbonization. Full article

Documentary videos on green energy consumption are widely distributed via platforms such as YouTube, yet the verbal framing strategies embedded in their subtitle transcripts remain systematically understudied. This study applies the Analysis of Topic Model Networks (ATMN)—an unsupervised machine learning approach combining LDA topic modeling, semantic network analysis, and hierarchical clustering—to subtitle transcripts extracted from 60 YouTube green energy consumption documentaries. Three distinct framing communities are identified: (1) the Technological Supply Frame, which foregrounds zero-carbon resources, renewable generation, smart grid systems, and AI-enabled energy management as the technical foundation of decarbonization; (2) the Socioeconomic Transition Frame, the most thematically expansive, which positions the energy transition simultaneously as an economic opportunity, a behavioral imperative, and a systemic industrial transformation spanning green investment, end-use substitution, industrial decarbonization, and green mobility; and (3) the Ecological Governance Frame, which integrates ecological co-benefits with international climate commitments to construct the transition as a globally mandated planetary responsibility. Together, these frames reveal a richer and more multi-dimensional verbal framing landscape than previously documented in the green energy communication literature, extending beyond techno-optimism or environmentalism to encompass financial, governance, and behavioral dimensions within a single integrated corpus. The identified framing strategies offer actionable guidance for policymakers, energy enterprises, and media producers seeking to accelerate green energy consumption transition through targeted, evidence-based video communication. Full article

Background: Interest in green hydrogen (GH) is increasing, as it can act both as an energy carrier and as an industrial feedstock to decarbonise applications that currently rely on fossil-based (grey) hydrogen. Hard-to-abate industries, such as steelmaking, face complex and multi-dimensional uncertainties when assessing conversion to GH and the associated supply chain redesign. Materials and Methods: We propose an enterprise-oriented decision-modelling framework that structures conversion drivers into six decision-relevant dimensions (socio-economic, infrastructure, technology, market, supply chain, and enterprise). The framework is refined through a two-round expert elicitation process and operationalised through a scenario planning workflow based on discrete key-factor projections and an elicited interdependency network. Building on this dependency structure, we propose a transparent consistency-based reduction approach that integrates pairwise projection compatibility and graph-guided screening to identify internally coherent and decision-relevant scenarios. The procedure is further demonstrated through an illustrative steelmaking conversion case. Results: The expert-supported workflow identifies 14 external key factors and their decision-relevant projections, together with an elicited interdependency structure among them. The illustrative application shows how an initial scenario space of 6561 configurations, based on eight selected key factors, can be screened to 1335 internally admissible configurations and consolidated into four representative scenarios. These scenarios capture distinct decision contexts, including coordinated acceleration, demand-led but infrastructure-constrained transition, technology and policy push with limited market pull, and fragmented, delayed transition. Conclusions: The approach enhances methodological transparency in scenario-based decision support and offers hard-to-abate industries a structured basis for evaluating green hydrogen conversion under systemic interdependencies and deep uncertainty. The illustrative application further demonstrates how the framework can transform combinatorial uncertainty into a compact and interpretable set of scenarios supporting stakeholder discussion and strategic decision-making. Full article

Achieving net-zero emissions requires balancing decarbonization with sustained enterprise development. Using panel data on China’s A-share listed firms from 2011 to 2023, this study examines whether regional carbon emission trading rights (CETR) pilots promote enterprise upgrading, proxied by the New Quality Productive Forces (NQPF) index. A staggered multi-period difference-in-differences framework shows that the CETR policy significantly increases enterprise NQPF (coefficient 0.059, p < 0.05). This finding remains robust after parallel trend tests, placebo simulations, propensity score matching, controlling for overlapping environmental policies, and using alternative outcome measures. Channel analyses indicate that CETR affects NQPF through two pathways: easing financing constraints (coefficient −0.019, p < 0.01) and accelerating digital transformation (coefficient 0.102, p < 0.01). The positive policy effect is stronger among non-state-owned enterprises and among firms whose senior managers lack financial backgrounds or do not hold concurrent positions in shareholder units. These results demonstrate that carbon trading drives systemic enterprise upgrading via resource and technology channels, with important heterogeneity across ownership and governance structures. Full article

Data centers are essential to modern infrastructure, but are significant contributors to greenhouse gas (GHG) and related environmental challenges. Despite energy efficiency improvements, rising electricity demands driven by technologies like AI pose challenges to sustaining low-emission sector growth. Regulatory requirements mandate data center operators to report electricity use and emissions, yet current methods, particularly pertaining to indirect sources, remain insufficient. This study explores how life cycle assessment (LCA)-based estimates of data center IT equipment impacts compared with commonly used corporate GHG accounting methods: average-data and spend-based. Environmental impacts were modeled at both grouped and granular levels to support enterprise-wide reporting and operational decision-making. Sensitivity and uncertainty analysis validate the robustness of the LCA models. Scenario analysis was also conducted to assess emission abatement strategies, including on-site renewable energy generation and operation with a low-carbon electricity grid. The results show that the LCA approach produced emissions of 0.710 CO₂ eq/kWh, along with additional burdens that are not captured through carbon-only metrics. The LCA results are closely aligned with the average-data method (0.723 kg CO₂ eq/kWh) while the spend-based method yielded substantially higher estimates (1.07 kg CO₂ eq/kWh), highlighting the inaccuracies associated with volatile market prices. Scenario analysis identifies grid decarbonization as the most effective mitigation pathway, while also demonstrating environmental trade-offs across impact categories. Findings highlight the importance of comprehensive LCA-based assessment methods to improve emission reporting accuracy, transparency, and sustainability-focused decision-making in the data center sector. Full article

This study systematically explores the underlying mechanisms of collaborative innovation driving the green transformation of traditional energy enterprises. Existing research primarily focuses on enterprise scale and overall competitiveness, rarely delving into these specific collaborative pathways. Furthermore, studies employing evolutionary game theory to analyze the tripartite relationship among the government, traditional energy, and emerging technology enterprises remain fragmented, failing to fully capture the dynamic mechanisms of multi-stakeholder strategic choices. To bridge these gaps, this paper constructs a tripartite evolutionary game model incorporating coordination costs and the benefit distribution ratio to explore their influence mechanisms. Replicator dynamics equations are employed to identify stable cooperation conditions, overcoming traditional two-party framework constraints. Additionally, MATLAB R2024b numerical simulations validate the theoretical findings. The results reveal two evolutionarily stable equilibrium points. First, higher initial willingness among participants accelerates the system’s evolution toward a stable cooperative state. Second, coordination costs induced by information asymmetry act as a core bottleneck that deters participation and risks collaborative collapse. Third, targeted government incentives and a rational benefit distribution ratio directly determine cooperation willingness; notably, enterprises adopt collaborative strategies only when this ratio falls between 0.27 and 0.69. Fourth, fair and transparent supervision is crucial for mitigating trust deficits and distribution disputes. Ultimately, scientifically designing incentives, optimizing benefit structures, promoting information sharing, and establishing robust supervision effectively facilitate a sustainable tripartite collaborative innovation pattern. Full article

Power grid technical renovation projects are implemented through project-based supply chains involving equipment procurement, logistics coordination and on-site construction under market, delivery and carbon constraints. Their final cost is jointly affected by engineering quantities, supplier behavior, lead-time uncertainty, material price volatility and sustainability requirements. Existing studies usually emphasize technical parameters and direct expenditure, whereas supplier reliability, green procurement, carbon intensity and procurement contingency effects are only indirectly incorporated. This study develops a dynamic Bayesian model for carbon-adjusted cost forecasting and investment priority support in power grid technical renovation projects. Based on 800 anonymized project-level records, a random forest is first used to identify informative engineering, supply chain and sustainability variables. These variables are then organized in a Bayesian network that links observed evidence, intermediate cost nodes and the carbon-adjusted cost target. A dynamic evidence-weighting mechanism updates posterior cost beliefs as supplier, logistics, market and carbon information become available during implementation. Compared with static Bayesian inference, XGBoost, an improved BPNN and GRA-based benchmarks, the proposed model yields lower MAE and RMSE. Ablation and scenario analyses further show that supply chain and sustainability variables improve both predictive performance and decision interpretability. The results provide a quantitative basis for budget control, green procurement adjustment, contingency allocation and sustainable asset renewal prioritization in energy enterprises. Full article

The clean and low-carbon transition of new-type power systems imposes increasingly stringent demands on green technology innovation among enterprises along the power industry chain. Identifying the drivers and potential remedies for green innovation bubble can offer China-originated solutions to the sustainable development of the global power sector. This paper focuses on Chinese power industry chain enterprises over the period 2016–2023. Drawing on the AMO framework, a three-dimensional analytical framework encompassing ability, motivation, and opportunity is developed. Double machine learning (DDML) is employed to perform benchmark regression and causal identification. Subsequently, gradient boosting trees (GBT) combined with SHAP interpretability analysis are applied to uncover nonlinear relationships and heterogeneous transmission pathways among key variables. The results indicate that energy-saving policies and green financial policies significantly inhibit the formation of the green innovation bubble in power industry chain enterprises. Specifically, these policies curb the green innovation bubble via three channels: an innovation incentive management mechanism, a peer imitation and convergence mechanism, and an industrial chain technology spillover mechanism. Upstream enterprises exhibit greater sensitivity to direct regulatory measures and backward technology spillovers from energy-saving and green finance policies, whereas midstream enterprises are more reliant on peer carbon emission pressure. The findings are validated through cross-verification among DDML, mechanism analysis, and interpretable analysis. The results provide empirical evidence and policy implications for optimizing energy-saving and green finance policies and for precisely deflating the green innovation bubble. Full article

Under the dual-carbon goals and energy transition backdrop, the photovoltaic cell has become a crucial pillar for optimizing China’s energy structure and promoting green development. From the perspective of patent intelligence, this study systematically investigates the spatiotemporal evolution paths, coupling characteristics, and driving mechanisms of China’s photovoltaic cell industry and innovation chains, using nationwide photovoltaic cell enterprise and patent data from 2005 to 2024 and integrating spatial gravity center modeling, location quotient analysis, and spatial Durbin models. The findings reveal the following: (1) the spatiotemporal evolution of the dual chains exhibits distinct phases, with a notable developmental leap after 2015. The industry chain shows a pattern of “westward shift and eastern optimization,” while the innovation chain evolves from eastern dominance toward a nationally coordinated, multipolar network. (2) At the macro level, the dual chains demonstrate a coupling trend characterized by “coordinated gravity center migration and spatial distance convergence,” yet significant spatial heterogeneity and mismatch persist at the city scale. (3) Industrial agglomeration has an inverted U-shaped effect on innovation, with regional heterogeneity in its impact, driven synergistically by multidimensional factors such as economic foundation, the innovation environment, and openness. Based on these insights, this study proposes recommendations for optimizing the spatial layout of these dual chains, strengthening multifactor synergy, and implementing regionally differentiated policies, aiming to provide decision-making references for achieving sustainable and high-quality development in the photovoltaic cell. Full article