Achieving net-zero carbon emissions requires context-sensitive and innovation-driven strategies, particularly in environmentally intensive sectors such as mining. This study examines how dynamic capabilities, specifically sensing, seizing and reconfiguring, facilitate circular economy (CE) innovations that support firms' progress towards NetZero objectives. Drawing on survey data from mining firms in Ghana, the study employs partial least squares structural equation modelling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to analyse both linear and configurational relationships. The results indicate that reconfiguring capabilities play a central role in perceived NetZero progress, particularly when combined with CE-based process innovations. Environmental dynamism further strengthens the positive relationship between CE innovations and progress towards NetZero. By clarifying how organisational capabilities and CE innovation types interact under dynamic conditions, this study contributes to the literature on sustainability transitions and offers practical insights for mining firms and policymakers seeking to advance NetZero-oriented strategies in resource-intensive and volatile environments for sustainable development.

Industry 4.0 technologies have been regarded as powerful means to enhance a firm's competitiveness in the Internet of Things environment. However, implementing Industry 4.0 technologies calls for considerable capital expenditure and might interrupt normal production in the short term. This study conducts an empirical analysis of the impact of investing in Industry 4.0 technologies based on a sample of 563 investment announcements of publicly listed firms on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2013 to 2018. Using the event study method, we find empirical evidence that these investment announcements lead to positive stock market reactions and improved financial performance. In particular, we empirically evaluate firms' short- and long-term stock prices and financial measures by considering the type of investments (i.e., digital or physical investments), whether the investment is product-oriented or manufacturing process-oriented, and whether the technologies are applied within a firm or across the supply chain. Our empirical findings hold true when a firm's strategic decision is accounted for and remain robust through various tests. Furthermore, we propose a decision framework for firms to balance the tradeoff between short-term disruption and long-term benefits resulting from an investment in Industry 4.0. Specifically, we develop a two-period model and investigate when and to what extent a firm should invest in Industry 4.0 technologies. Empirical and modeling analyses provide managerial insights for firms that grapple with the net benefit of investment in Industry 4.0.

The low-carbon transition of energy systems is crucial for fulfilling global climate commitments. However, transition pathways are profoundly constrained by multidimensional environmental, economic, and security objectives. A significant research gap remains in planning energy system transitions that balance these multifaceted requirements, particularly in quantifying their impact on technology selection under ambitious decarbonization targets like carbon neutrality. To bridge this gap, this study develops a hybrid planning framework integrating hourly smart energy system simulation with multi-objective optimization algorithms. This framework comprehensively accounts for all energy sectors, key cross-sectoral measures, and resource assessment. Using China's energy system as a case study, the proposed framework evaluates the feasibility of achieving carbon neutrality by 2060 and identifies Pareto-optimal transition schemes under competing constraints. Compared to conventional scenario development, the optimal scheme derived from this framework reduces CO2 emissions by 7.64 million tons while lowering energy system costs by 2.5%, thereby demonstrating effective multi-objective coordination. Moreover, the analysis reveals that technological deployment trajectories are highly contingent upon the prioritization of specific objectives. Beyond optimization, this study integrates a resource assessment, revealing that while land resources remain abundant, the transition faces acute supply chain risks for critical minerals like Cobalt and Nickel. Consequently, the proposed framework offers a scalable tool for designing low-carbon transition strategies across diverse regions, providing critical insights for meeting ambitious decarbonization targets.

The textile industry is a major contributor to global resource depletion and waste generation, making sustainability, Environmental, Social, and Governance (ESG) performance, and circularity central priorities for transforming its supply chains (SCs). While sustainability provides the overarching goal of reducing environmental burdens and promoting responsible production, ESG offers a structured framework for evaluating a firm’s environmental performance, social responsibility, and governance practices. Circularity, a core principle of the Circular Economy (CE) paradigm, serves as a key pillar of sustainable transformation by operationalising these goals through material recovery, resource efficiency, and closed-loop flows. However, effective adoption of circular practices depends heavily on selecting suppliers who perform strongly across both ESG and circularity dimensions. This area remains underexplored, especially within Indian textile micro, small, and medium enterprises (MSMEs). To address this gap, this study develops a multi-criteria decision-making (MCDM) framework to identify the most suitable sustainable circular supplier (SCS) of recycled cotton yarn. Criteria were identified through an extensive literature review and expert consultations, and evaluated using an integrated Weighted Influence Non-linear Gauge System (WINGS) and Multi-Attributive Border Approximation Area Comparison (MABAC) approach. WINGS captures the interdependencies among sustainability-oriented ESG-circular criteria, while MABAC ranks suppliers based on their overall performance. The results show that collaboration for circularity, recycling capability, green certifications, governance compliance, product quality, and SC resilience are the most influential factors driving SCS selection. Overall, the proposed framework provides a practical decision-support tool for textile firms seeking to strengthen sustainability, enhance ESG alignment, and accelerate CE adoption, thereby enabling more responsible and competitive operations. The framework’s real-world applicability is demonstrated through a case study of an Indian textile MSME.

This paper focuses on a scenario in which buyers require non-compliant suppliers to compensate for emission-reduction shortfalls through carbon trading. We consider a two-echelon low-carbon supply chain consisting of two buyers sharing a common supplier. We employ a simultaneous game model to analyze equilibrium decisions on carbon purchasing, misreporting, and audit effort across four strategies: non-audit, independent audit, shared audit, and joint audit. We further explore the impact of key parameters, such as the carbon price. The findings indicate that: 1) The joint audit strategy is the most effective in suppressing supplier misreporting, whereas the non-audit strategy is the least effective. The difference in misreporting levels between shared and independent audits is moderated jointly by the greenwashing penalty coefficient and the carbon price. 2) Supplier profit is maximized under the non-audit strategy and minimized under the joint audit strategy. Buyer profit is consistently higher with auditing than without, while the profitability ranking between shared and joint audits depends on the carbon price. 3) The supplier’s emission reduction effort increases with the carbon price and the greenwashing penalty coefficient. However, the misreporting level is positively correlated with the carbon price and the greenwashing penalty coefficient, but negatively correlated with the misreporting penalty and misreporting cost. Furthermore, buyers’ audit effort increases with the carbon price but decreases as the misreporting cost rises.

Corporate environmental, social, and governance (ESG) performance has come under increasing scrutiny by diverse stakeholders. A basic question is whether ESG performance makes a difference. One important relationship is whether ESG performance relates to firm efficiency. This study aims to investigate this relationship, which has yielded varying results. We also explore how an emerging multi-stakeholder technology—blockchain technology—affects the relationship between ESG performance and efficiency. Using a matched sample of 420 Chinese listed companies, we employ a super-efficiency slacks-based data envelopment analysis (SBM-DEA) model to estimate firm efficiency. The subsequent analysis utilizes a Tobit regression model to evaluate the relationships between various ESG dimensions and efficiency. The empirical results indicate that the environmental dimension has a significant positive relationship to firm efficiency, while the social dimension has a significant negative relationship to firm efficiency. The existence of blockchain technology indicates a significant moderating effect on the relationship between environmental (and social) performance and firm efficiency. This study also highlights the relationships between contextual factors and firm efficiency, with firm revenue growth, supply chain concentration and industry strongly related to firm efficiency. Explanations for these and other findings are discussed—the results call for additional research.

Green finance plays a crucial role in reducing corporate carbon emissions. However, the mechanisms linking green finance to emission reduction remain underexplored. This study examines 1399 Chinese listed companies from 2013 to 2022 to evaluate the carbon-reducing effects of the Green Financial Reform and Innovation Pilot Zone (GFRI). Using a two-stage value chain framework, we decompose green innovation into green technology research and development and green outcomes transformation to analyze the transmission mechanisms. The results show three key findings. First, the implementation of the GFRI significantly reduces corporate carbon emissions, and the results are robust across specifications. Second, the policy effect is stronger among firms in the central and eastern regions and in the manufacturing sector. Third, the carbon reduction effect of the GFRI is primarily driven by improvements in green innovation efficiency. Green outcomes transformation efficiency plays a more critical role than green technology research and development efficiency. These findings suggest that firms should accelerate green innovation processes and strengthen internal regulatory mechanisms to increase the effectiveness of green finance policies in promoting carbon reduction.

Environmental, social, and governance (ESG) practices and performance are of interest to diverse stakeholders. Organizations know they have to respond to this increased interest. Organizations, and scholarly research, have questions on whether ESG performance and firm operational efficiency relate to each other. Previous evidence on this relationship is mixed. This study delves more deeply to help identify whether context, especially supply chain characteristics provide insights into this relationship. Thus, we explore how an important external supply chain relational characteristic-supply chain concentration-moderates this relationship. We use a sample of 41,717 firm-year observations of 4772 Chinese A-share listed companies from 2010 to 2023 to investigate these relationships. The findings reveal that environmental dimensions have a significant negative relationship to corporate operational efficiency. Social and governance dimensions have significant positive relationships to corporate operational efficiency. Interestingly, there is an asymmetric moderation result. Supplier concentration significantly moderates the relationship between environmental performance and operational efficiency; customer concentration significantly moderates the relationships between both social and governance performance and operational efficiency. The study also sheds light on the relationships between contextual factors and operational efficiency, with firm age, board independence, revenue growth and financial leverage all significantly relate to corporate efficiency. The research findings provide valuable insights for practitioners. The findings also bring up additional questions for supply chain sustainability researchers.

Energy sharing in distributed energy systems constitutes the pivotal strategy development for enhancing clean energy utilization and low-carbon emission achievement. However, as market mechanisms continue to improve, energy trading in distributed energy systems will shift from the traditional system-to-system model to the multi- stakeholder model. Therefore, this study constructs a two-layer energy-sharing framework that contains different stakeholders. Firstly, the energy system operator guides the energy-sharing behavior among distributed energy systems through energy transaction pricing to maximize its revenue. Then, the distributed energy systems obtain optimal energy sharing and internal operation strategies based on the energy system operator's price signals to minimize their energy costs. Additionally, this study addresses the uncertainty of renewable energy generation in distributed energy systems using the Wasserstein metric ambiguity set, and combines it with the energy sharing issue to form a distributionally robust energy trading optimization model. Finally, to solve the two-layer multi- agent distributionally robust energy sharing problem, we employ strong duality theory to transform the problem into a more solvable form. An adaptive genetic algorithm-analytical target cascading method is proposed to achieve optimal transaction pricing and energy scheduling. The case analysis results demonstrate that the proposed strategy can achieve economic benefits of 5.51 % and environmental benefits of 5.73 %, effectively balancing economic efficiency and robustness.

Environmental challenges and increasing resource consumption may be mitigated through organizational circular economy (CE) practices. Implementing CE practices requires organizations to rethink, develop, and implement new initiatives and processes. It has been argued that blockchain technology (BCT) can support corporate and supply chain CE practices. However, empirical evidence on whether BCT adoption can complement corporate CE practices when considering firm financial performance is virtually non-existent. Using the resource-based view and a dataset of 1766 firm-year observations of Chinese listed companies, we investigate the relationship between corporate CE practices and financial performance, as well as the moderating effect of BCT adoption. Initial findings reveal a significantly positive relationship between corporate CE practices and financial performance. However, counterintuitively, BCT adoption not only directly negatively relates to firm financial performance but also weakens the positive relationship between CE practices and financial performance. Further analysis found that these direct and indirect negative effects of BCT adoption are only observed in resource-constrained firms, supporting our argument from a resource scarcity perspective. This study provides new insights into the nuanced relationship among CE practices, BCT adoption, and financial performance from the resource-based view. These insights provide new and valuable guidance for researchers and practitioners.

Amidst evolving consumer preferences for eco-friendly products, green technology investment has become a crucial strategic consideration for manufacturers. This study investigates green technology investment choices among competing manufacturers within the carbon emission trading (CET) market. Using a Nash game model, we analyze evolutionary stability strategies for green technology investment through evolutionary game theory, uniquely accounting for the impact of both green competition intensity and price competition. Our findings highlight that manufacturers should be more assertive in pursuing green technology investments amid intensified price competition. Moreover, the propensity for such investments rises with increased green competition, despite higher costs. Additionally, we explore the influence of carbon markets and consumer sensitivity, discovering that reasonable carbon quota allocation, higher carbon pricing, and greater consumer environmental awareness collectively bolster manufacturers' inclination toward green technology investments. This inquiry underscores the nexus of knowledge innovation, environmental consciousness, and market dynamics, providing profound insights into manufacturers' strategic trajectories in sustainable innovation.

Reverse logistics (RL) creates a number of opportunities for electronic equipment manufacturing enterprises and helps to augment the sustainability of a supply chain (SC). In India, outsourcing to Third Party Reverse Logistics Providers (3PRLPs) is a prominent initiative for a successful RL business model. However, small and mid-size enterprises (SMEs) face a number of risks in outsourcing RL activities. In this context, this study fills the literature gap by identifying and analysing the risk factors associated with outsourcing to 3PRLPs by SMEs in electronics sectors in India for a successful and sustainable reverse supply chain (RSC). Initially, 30 risk factors in the context of RL outsourcing are identified and clustered into 6 risk categories, namely “relationship and competence risks”, “economic risks”, “operational risks”, “organizational risks”, “social risks”, and “environmental risks”. Then, a weighted influence non-linear gauge system (WINGS) is used to understand the level of importance of each risk category and, simultaneously, they are also classified in cause and result groups. Similarly, risk factors within each risk category are evaluated. The findings indicate that SMEs in the electronics sector in India encounter several risks in outsourcing RL activities like uncertainty in returns, financial burden, stiff competitions, 3PRLP opportunism, and many more. Hence, an immediate intervention is recommended for SMEs in the electronics sector in India to overcome the risk factors under the “economic” and “relationship and competence” risk categories to implement sustainable and business efficient RL through outsourcing to 3PRLP. The implications drawn from the study will guide the RL team experts of SMEs to deal with risks in the course of outsourcing to 3PRLP.

The vigorous development of new energy sources, such as photovoltaic (PV) systems, is essential to achieving the goals of “Carbon Neutrality and Carbon Peak.” Clarifying the inherent logic of sustainable PV development and effectively evaluating and optimizing PV projects are urgent priorities. To address this, the study proposes a data-driven framework for the sustainable development of PV systems. First, topic modeling is employed to analyze existing PV research, identify mainstream topics, and outline the critical pathways for sustainable development. Second, focusing on a primary pathway, a two-stage decision-making model based on text mining is introduced to evaluate and optimize the target PV system. In the first stage, text mining is integrated with an adaptive multi-objective particle swarm optimization algorithm to generate Pareto solutions across three objectives: lifecycle cost, loss of power supply probability, and global warming potential of greenhouse gas emissions. In the second stage, stochastic multicriteria acceptability analysis is applied to select the optimal Pareto solution. The framework is validated through its application to a real-world case involving PV carport configuration in Chengdu, China. This study not only establishes an objective and comprehensive research paradigm for analyzing PV system sustainability through text analysis but also provides decision-making references for researchers and industry practitioners to better understand the development logic and pathways for sustainable PV system configuration.

Low-carbon supply chain (LCSC) companies mitigate their carbon footprint through direct emission reduction (DER) efforts, such as using renewable energy, and through emissions trading for indirect offset. Blockchain technology, increasingly adopted in sectors, such as fashion and automotive, supports these methods by addressing key challenges. However, the optimal adoption mode-government, manufacturer, or retailer-remains unclear. In this article, we integrate blockchain's three benefits (reduced DER cost, lower transaction cost in emissions trading, and enhanced green trust) and its three costs (setup cost, operational cost, and usage fee) to compare these modes based on the LCSC's sustainability, profitability, and social welfare. Key findings are as follows. First, blockchain adoption typically boosts sustainability and profitability, although two tradeoffs require careful consideration. Second, retailer adoption outperforms the other modes by significantly enhancing sustainability, maximizing blockchain user profits, and achieving the highest social welfare outcomes. The retailer's profit can also be maximized if the setup cost coefficient and total carbon footprint level are sufficiently low. Another advantage of this mode lies in the flexibility of usage fee. Third, the impact of blockchain adoption varies across supply chains with different carbon footprint levels. These results remain robust in extension with risk considerations. This study provides valuable managerial insights, including guidance for LCSC on selecting the optimal blockchain adoption mode, and recommendations for policymakers to address the lack of economic incentives in low-emission supply chains and reduce the overreliance on indirect offset in high-emission ones.

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Environmental regulations are important organizational strategic drivers. Environmental regulatory mechanisms include "carrots" or "sticks"-they can be incentive or coercion-based. This study empirically investigates stock market reactions to various environmental regulatory mechanisms. Using a sample of 334 environmental regulatory events reported by 212 listed Chinese firms between 2010 and 2020, we find that monetary reward has a greater positive market reaction towards governmental reward than non-monetary reward. We also find that governmental operational disruption penalties have a greater negative market reaction than operational non-disruption penalties. Governmental levels of the environmental regulation implementation subject do not play a moderating role in the relationship between governmental regulations and firm market value. These findings provide various policy and organizational insights as businesses seek to meet legitimacy gains in response environmental regulatory mechanisms.

Sustainable agriculture has emerged as a critical topic in the context of sustainable development goals set by the United Nations. A key aspect impacting the sustainability of the agriculture supply chain is the usage of agrochemicals. Transitioning to sustainable alternatives from agrochemicals poses challenges, as it affects farms’ productivity, income, and food supply to the market. The delicate balance between farm income and greenhouse gas emissions related to chemical fertilizer usage has not been addressed adequately using a dynamic system behavior perspective. This study employs a System Dynamics model to simulate the impact of adopting biofertilizers on the triple-bottom-line performance of the agrochemical supply chain from a policy perspective. The model aims to understand stakeholder behavior within the fertilizer supply chain and enhance its sustainability. Additionally, the study models the effects of various input subsidies using the design of experiments in an Indian agrochemical supply chain, examining trade-offs involved in the triple-bottom-line (social, environmental, and economic) parameters for each subsidy. The simulation model offers policymakers insights into determining appropriate subsidy levels to facilitate a sustainable transition of agricultural supply chains. In this context, various possible scenarios were obtained by simulating the policy parameters (agriproduct price, chemical fertilizer prices, biofertilizer fixed costs, and biofertilizer subsidies) resulting in optimal levels of environmental impact, producer profit, and social benefit. It also provides a comprehensive evaluation of the triple-bottom-line effects of policy strategies, thereby facilitating the comprehension of trade-offs in the supply chains of lower/middle-income countries. The study contributes valuable guidance for policymakers to make informed decisions for promoting sustainable agriculture and achieving the triple-bottom-line objectives in the agrochemical industry.

Interconnected distributed energy systems (DESs) can facilitate multi-energy consumption, improve energy efficiency, and advance decarbonization goals. In this context, this study proposes an energy sharing framework that considers multiple uncertainties to optimize the low-carbon robust economic operation of interconnected DESs. First, a low-carbon dispatch model for DESs that includes electricity and heat sharing, integrated demand response (IDR), and low-carbon policies is constructed. Then, a two-stage robust optimization model is developed considering the source-load uncertainty, and the Karush-Kuhn-Tucker (KKT) condition is introduced to transform the max-min problem in the second stage into a single-layer issue. In addition, an approach combining the alternating direction multiplier method (ADMM) with the column-and-constraint generation algorithm (CCG) is proposed for a distributed and hierarchical solving of the two-stage energy sharing problem. Finally, to address the issue of transactional payments for energy sharing, a profit allocation model based on multi-factor contributions is developed to ensure that the benefits generated by the sharing system are fairly distributed. Based on actual data simulation, the effectiveness of the two-stage robust sharing scheme presented in this study is demonstrated for economy and carbon reduction.