Search Results (40)

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

The energy sector uses artificial intelligence (AI) as a crucial instrument to achieve environmental sustainability targets by improving resource efficiency and decreasing emissions while minimizing waste production. This paper establishes an industry-specific executive playbook that guides energy sector leaders by implementing AI technologies for sustainability management with approaches suitable for industrial needs. The playbook provides an industry-specific framework along with strategies and AI-based solutions to help organizations overcome their sustainability challenges. Predictive analytics combined with smart grid management implemented through AI applications produced 15% less energy waste and reduced carbon emissions by 20% according to industry pilot project data. AI has proven its transformative capabilities by optimizing energy consumption while detecting inefficiencies to create both operational improvements and cost savings. The real-time monitoring capabilities of AI systems help companies meet strict environmental regulations and international climate goals by optimizing resource use and waste reduction, supporting circular economy practices for sustainable operations and enduring profitability. Leaders can establish impactful technology-based sustainability initiatives through the playbook which addresses the energy sector requirements for corporate goals and regulatory standards. Full article

As global climate change intensifies and carbon emission policies become increasingly stringent, carbon risk has emerged as a crucial factor influencing corporate operations and financial markets. Based on data from A-share listed companies in China from 2009 to 2022, this paper empirically examines the pricing mechanism of carbon risk in the Chinese capital market and explores how different corporate signaling behaviors affect the carbon risk premium. The findings reveal the following: (1) Carbon risk exhibits a significant positive premium (annualized at about 1.33% per standard deviation), which remains robust over longer time windows and after replacing the measurement variables. (2) Heterogeneity analysis shows that the carbon risk premium is not significant in high-energy-consuming industries or before the signing of the Paris Agreement, possibly due to changes in investor expectations and increased green awareness. Additionally, a significant difference in the carbon risk premium exists between brown and green stocks, reflecting a “labeling effect” of green attributes. (3) Issuing green bonds, as an active corporate signaling behavior, effectively mitigates the carbon risk premium, indicating that market investors highly recognize and favor firms that actively convey green signals. (4) A “greenwashing” indicator constructed from textual analysis of environmental information disclosure suggests that greenwashing leads to a mispricing of the carbon risk premium. Companies that issue false green signals—publicly committing to environmental protection but failing to implement corresponding emission reduction measures—may mislead investors and create adverse selection problems. Finally, this paper provides recommendations for corporate carbon risk management and policy formulation, offering insights for both research and practice in the field. Full article

As China’s voluntary greenhouse gas emission reduction mechanism undergoes institutional revitalization, the accurate valuation of carbon assets such as China Certified Emission Reductions (CCERs) becomes increasingly critical for effective climate finance and sustainability-oriented investment. This study proposes an integrated value assessment model for CCERs that combines Long Short-Term Memory (LSTM) neural network-based carbon price forecasting with both the discounted net cash flow method and the Black–Scholes option pricing framework. Applying this model to a wind power project, the study found that the practical value of CCERs, derived from verified emission reductions, significantly exceeds their market option value, underscoring the economic and environmental viability of such projects. By distinguishing between the realized and potential values of carbon credits, this research offers a comprehensive tool for carbon asset valuation that supports corporate carbon management and policy development. The framework contributes to the growing literature on sustainable finance by aligning carbon asset pricing with long-term climate goals and enhancing transparency in carbon markets. Full article

The paper presents the design and development of a universal simulation model named SustainSIM, intended for optimizing the carbon footprint in mechanical engineering production. The objective of this model is to enable enterprises to accurately quantify, monitor, and simulate CO₂ emissions generated during various manufacturing processes, thereby identifying and evaluating effective reduction strategies. The paper thoroughly examines methodologies for data collection and processing, determination of emission factors, and categorization of emissions (Scope 1 and Scope 2), utilizing standards such as the GHG Protocol and associated databases. Through a digital simulation environment created in Unity Engine, the model interactively visualizes the impacts of implementing green technologies—such as solar panels, electric vehicles, and heat pumps—on reducing the overall carbon footprint. The practical applicability of the model was validated using a mechanical engineering company as a case study, where simulations confirmed the model’s potential in supporting sustainable decision-making and production process optimization. The findings suggest that the implementation of such a tool can significantly contribute to environmentally responsible management and the reduction of industrial emissions. In comparison to existing methods such as SimaPro/OpenLCA (detailed LCA) and the Corporate Calculator (GHG Protocol), SustainSIM achieves the same accuracy in calculating Scopes 1/2, while reducing the analysis time to less than 15% and decreasing the requirements for expertise. Unlike simulation packages like Energy Plus, users can modify parameters without scripting, and they can see the immediate impact in CO₂e. Full article

This study investigates how firms in emerging markets respond to climate policy risk, with a particular focus on the dynamic role of green innovation in shaping carbon information disclosure. Using a difference-in-differences (DID) framework, we examine the impact of China’s 2018 carbon reporting policy, which represents an institutionally significant but non-mandatory regulatory intervention, on the disclosure behaviors of A-share listed firms from 2013 to 2022. The results show that the policy significantly increased firms’ attention to carbon information disclosure, especially among those with limited green innovation capacity. In contrast, firms with stronger innovation capabilities exhibited more stable disclosure practices, suggesting a buffering effect against regulatory uncertainty. Further analysis reveals that the moderating effect of green innovation changes over time, as innovation-oriented firms gradually adjust their disclosure strategies in response to evolving policy expectations. These findings highlight green innovation as a key internal resource that enables firms to strategically adapt to climate policy risks. This study contributes to the literature on climate risk management and corporate sustainability by providing empirical evidence on how dynamic capabilities shape disclosure outcomes and risk management strategies under changing regulatory conditions. Full article

Amid escalating global climate challenges, the interplay between corporate climate risk disclosure and green technological innovation has become a pivotal scholarly focus in sustainability research. This study empirically examines the impact of climate risk disclosure on corporate green technology innovation and its underlying mechanisms using data from China’s A-share listed companies spanning 2004 to 2022. Key findings reveal that climate risk information disclosure significantly enhances green innovation capabilities through dual pathways: elevating media attention and reducing agency costs. Specifically, media scrutiny exerts external pressure via reputational incentives and public oversight, driving firms to accelerate green technology deployment. Concurrently, reduced agency costs mitigate information asymmetry between shareholders and management, enabling optimized resource allocation for long-term innovation investments. Heterogeneity analysis indicates that this catalytic effect is more pronounced in larger firms and those facing lower financing constraints. The research theoretically and practically elucidates the dual mechanisms through which climate disclosure propels green innovation, providing empirical support for refining corporate sustainability reporting systems and recalibrating regulatory frameworks. Policy recommendations include adopting differentiated climate disclosure standards, strengthening media and investor oversight, and incentivizing green innovation through executive performance metrics to facilitate low-carbon economic transition. Full article

In the context of economic globalization, international trade facilitates cross-border production and consumption but raises concerns such as carbon transfer from corporate trade activities. This study investigates the influence of executives’ green awareness on the global green value chain (GGVC) using matched data from Chinese customs and enterprises, along with newly constructed GVC net flow indicators and econometric models. From 2007 to 2016, executives’ green awareness in Chinese A-share listed companies significantly promoted GVC development, with proactive, green-oriented executives driving greater green upgrades. Variations in executives’ backgrounds, firm types, regional education levels, and highly digitalized production environments further shaped the effectiveness of green leadership. The findings provide empirical evidence and insights into green export management practices. Full article

Urban road traffic congestion has become a serious issue for cold chain logistics in terms of delivery time, distribution cost, product freshness, and even organization revenue and reputation. This study focuses on the cold chain distribution path by considering road traffic congestion with transportation, real-time vehicle delivery speeds, and multiple-vehicle conditions. Therefore, a vehicle routing optimization model has been established with the objectives of minimizing costs, reducing carbon emissions, and maintaining cargo freshness, and a multi-objective hybrid genetic algorithm has been developed in combination with large neighborhood search (LNSNSGA-III) for leveraging strong local search capabilities, optimizing delivery routes, and enhancing delivery efficiency. Moreover, by reasonably adjusting departure times, product freshness can be effectively enhanced. The vehicle combination strategy performs well across multiple indicators, particularly the three-type vehicle strategy. The results show that costs and carbon emissions are influenced by environmental and refrigeration temperature factors, providing a theoretical basis for cold chain management. This study highlights the harmonious optimization of cold chain coordination, balancing multiple constraints, ensuring efficient logistic system operation, and maintaining equilibrium across all dimensions, all of which reflect the concept of symmetry. In practice, these research findings can be applied to urban traffic management, delivery optimization, and cold chain logistics control to improve delivery efficiency, minimize operational costs, reduce carbon emissions, and enhance corporate competitiveness and customer satisfaction. Future research should focus on integrating complex traffic and real-time data to enhance algorithm adaptability and explore customized delivery strategies, thereby achieving more efficient and environmentally friendly logistics solutions. Full article

In the face of escalating environmental challenges, the imperative to reduce pollution and carbon emissions has never been more critical. Synergistic effects aimed at mitigating these pressing issues constitute the central concern of contemporary environmental governance and sustainable development in the global context. This paper adopts the Technology–Organization–Environment (TOE) framework and a configurational perspective to identify three driving pathways for achieving synergistic reduction in pollution and carbon emission (SRPCE), using a fuzzy-set qualitative comparative analysis (fsQCA) approach: the Dual-Wheel Type, the Well-Regulated Type, and the Effective Pull Type. The Dual-Wheel Type illustrates how combining these two strategies can lead to enhanced SRPCE. The Well-Regulated Type is driven by push factors while the Effective Pull Type is driven by pull factors. This study clarifies the interplay between front-end prevention and end-of-pipe treatment in environmental protection investments. It also broadens the understanding of environmental regulation’s impact on corporate environmental governance by highlighting the nuanced ways regulations shape organizational behavior. Finally, it advances research on synergistic effects by focusing on micro-enterprises. This research thus presents a comprehensive perspective on synergistic environmental effects and yields actionable insights, suggesting that the government can adopt synergistic policies while enterprises, through self-regulation and proactive management, can implement sustainable practices to achieve effective pollution reduction and carbon mitigation. Full article

This research focuses on developing an innovative platform to manage carbon footprint data related to office activities and monitor green spaces, integrating geoinformatics and machine learning technologies. The platform addresses the lack of automated systems for tracking carbon emissions, particularly in high paper consumption environments, such as those involving printing and photocopying. Additionally, it monitors green spaces within corporate settings, an aspect often overlooked in existing systems. The study demonstrates the platform’s capability to automate carbon footprint calculations and provide accurate assessments of green areas, achieving a high accuracy rate of 96.22% and a Kappa coefficient of 0.92. The results confirm the platform’s ability to deliver both comprehensive and granular environmental insights, supporting decision making for more sustainable office environments. The key novelty of this study lies in the integration of real-time data capture with geoinformatics and machine learning to efficiently track both carbon footprints and green spaces. This approach offers a practical solution to a gap in environmental data management in office settings, enabling organizations to align their practices with sustainability goals. The platform’s precise, automated system contributes significantly to the development of eco-friendly workplaces, highlighting its academic and practical value in the field of environmental sustainability. Full article

The Colorado and Mississippi River basins are crucial for water supply, agriculture, and ecological stability in the U.S., yet climate change, water management practices, and energy sector demands pose significant challenges to their sustainability. This paper highlights the potential of leveraging the Voluntary Carbon Market (VCM) to address these challenges by creating new revenue streams and incentivizing sustainable water management practices. It provides high-level estimates by extrapolating from existing literature. The paper finds that water projects in these basins could generate over 45 million carbon credits annually, potentially attracting around USD 4.5 billion in investments over the next decade. However, challenges such as high costs, complex regulations, and stakeholder coordination must be addressed. The paper also identifies opportunities for advancing water resiliency projects, including increasing public awareness, engaging corporations, and utilizing innovative financing mechanisms. Recommendations include promoting the VCM–water relationship, encouraging methodology innovation, developing pilot programs, investing in digital monitoring technologies, and conducting localized analysis to optimize carbon credit potential in water management. In conclusion, this paper quantifies the potential of water projects to generate carbon credits and indicates that integrating carbon markets with water management strategies can significantly contribute to global climate goals and improve water resilience in these critical regions. Full article

Amid China’s pursuit of a green and low-carbon transition, corporate social responsibility (CSR) is facing new challenges. Our research delves into the influencing factors and mechanisms for CSR reputation under green and low-carbon transition and provides practical enlightenment for enterprises to achieve sustainable development. This paper constructs a comprehensive index system of CSR from five dimensions (innovation, coordination, sustainability, openness, and sharing), and CSR reputation of China’s A-share listed companies is comprehensively estimated by using an entropy method and data from 2013 to 2021. Then, from the perspective of external supervision and internal governance, we discuss the influence factors of CSR reputation, with an emphasis on the impact of public environmental concerns. Finally, the realization mechanism of CSR is further revealed. It is found that public environmental concern and the expansion of the enterprise scale boost the enhancement of CSR reputation. However, a higher proportion of female managers tends to hinder CSR reputation. Furthermore, public environmental concern plays a more prominent role in improving CSR reputation of non-state-owned and eastern enterprises. Additionally, public environmental concern significantly enhances CSR reputation through green technology innovation and executive environmental awareness. This research provides valuable insights for improving CSR reputation and optimizing regulatory compliance and governance practices. Full article

Blockchain has revolutionized the field of supply chain management, allowing firms to optimize their operations and achieve enhanced levels of efficiency and environmental responsibility. This study investigates the relationships between the functionalities of the blockchain-enabled Internet of Things (IoT) and many aspects of the supply chain, namely, supplier integration, internal integration, customer integration, and sustainable logistic capabilities. It also analyses the impact of these correlations on the supply chain performance and sustainability of firms. This study is grounded in an empirical inquiry carried out using a questionnaire survey of the retail industry in Pakistan. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyze the data. The findings indicate a direct correlation between the use of BC-enabled Internet of Things (IoT) capabilities and the integration of the supply chain, as well as logistic capabilities. Furthermore, there is a strong and meaningful correlation between blockchain-enabled supply chain integration and sustainable logistic capabilities and firm performance. The research findings indicate that the combined impact of digital innovations results in higher levels of corporate performance and sustainability. By embracing digitization through blockchain technology, business organizations can attain supply chain excellence. This integration enhances the adoption of environmentally beneficial actions, minimizes waste, and decreases carbon emissions across supply chains, thus reinforcing the connection between sustainable firm performance and environmental sustainability. As a result, the enterprises may execute sustainably through improved logistical capabilities and effective integration. The results hold great theoretical and practical significance for improving supply chain management efficacy and accomplishing multiple SDGs, including SDG-8, SDG-9, SDG-11, and SDG-12. Full article

Calculating the carbon footprint (CF) holds paramount importance in today’s world as it provides a tangible measure of our impact on the environment. In the corporate realm, businesses armed with CF data can optimize operations, reduce waste, and adopt greener technologies, leading to both environmental and economic benefits. In this study, carbon emissions—a significant global issue—are investigated through the lens of the ISO 14067-ISO Product Based Carbon Footprint (CF) standard, focusing on the operations of a mold company. The primary innovation lies in meticulously tracing every stage of plastic bottle blow mold production, the most prevalent product in the mold industry, from its raw material input to its final form as a mold in the factory. Subsequently, detailed calculations and analysis are conducted to quantify the carbon footprint associated with this process and its impact on the environment. The calculated CF for one ton of PBBM produced by Petka Mold Industry is presented. This study fills a critical gap in the literature by providing a holistic understanding of the carbon footprint of plastic bottle blow mold (PBBM) production, thereby offering valuable insights for managing carbon emissions and promoting sustainability within the mold industry. By integrating a life cycle product carbon footprint thinking into industrial practices, a greener, more sustainable future can be paved, mitigating the ecological footprint of the PBBM. Full article