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This study aims to holistically explore the intersection of sustainable supply chain management, carbon accounting and life cycle assessment (LCA). The purpose of this study is to derive state-of-the-art knowledge of this landscape and use it to build a structured research agenda that can help to further develop this field.

Seventy-eight distinct contributions, identified through a rigorous, transparent and replicable search protocol, are analyzed through a systematic literature review. Additionally, bibliometric information on the studies is extracted from the Scopus database and visualized through the use of VOSViewer and RStudio.

This study outlines the current state-of-the-art knowledge across three spheres of supply chain management, synthesizing the extant literature into several nascent themes – namely, the significance of Scope 3 emissions; how LCA can be integrated into carbon accounting and sustainability disclosures, the differences between countries and industries in terms of carbon emissions and policies for a concerted effort toward carbon reduction.

Several practical implications can be drawn from the research for both practitioners and policymakers. The research provides a comprehensive, bird’s eye view on the discrepancies between different industries and countries in terms of carbon emissions, along with how carbon accounting should move toward strategic and predictive adoption.

The results show that adopting life cycle thinking can contribute to more transparent monitoring of carbon emissions in supply chains; however, its use in sustainability reporting needs to become more widespread, encompassing not only the carbon footprints of products and services but also the organization as a whole.

Using bibliographic and critical qualitative analyses, this study reviews the literature on LCA in sustainable supply chain management and carbon accounting. To the best of the authors’ knowledge, this review is the first attempt to synthesize this relevant and rapidly growing nexus between the three literature streams mentioned above.

Coordinating low-carbonization and digitalization is a practical implementation pathway to achieve high-quality economic development. Regions are under great emission reduction pressure to achieve low-carbon development. However, why and how regional emission reduction pressure influences enterprise digital transformation is lacking in the literature. This study empirically tests the impact of emission reduction pressure on enterprise digital transformation and its mechanism.

This article takes the data of non-financial listed companies from 2011 to 2020 as a sample. The digital transformation index is measured by entropy value method. The bidirectional fixed effect model was used to test the hypothesis.

The research results show that emission reduction pressure forces enterprise digital transformation. The mechanism lies in that emission reduction pressure improves digital transformation by promoting enterprise innovation, and digital economy moderates the nexus between emission reduction pressure and digital transformation. Furthermore, the effect of emission reduction pressure on digital transformation is more significant for non-state-owned, mature and high-tech enterprises.

This paper discusses the mediating role of enterprise innovation between carbon emission reduction pressure and enterprise digital transformation, as well as the moderating role of digital economy. The research expands the body of knowledge about dual carbon targets, digitization and technological innovation. The author’s findings help update the impact of regional digital economy development on enterprise digital transformation. It also provides theoretical guidance for the realization of digital transformation by enterprise innovation.

The interconnected challenges of climate change and social inclusivity have placed unprecedented pressure on businesses to adopt responsible practices. While previous research has explored the individual impacts of environmental, social, and governance (ESG) performance and diversity initiatives, there remains a dearth of comprehensive investigations into how these factors collectively influence carbon emission scores. Drawing on the legitimacy theory, we explore whether ESG and diversity scores predict global companies' carbon emission scores. As concerns about the environmental impact of businesses grow, understanding the relationships between ESG performance, diversity management, and carbon emissions becomes imperative for sustainable corporate practices.

The primary dataset for this study includes 1,268 worldwide firm-year data for 2021. The sample is subjected to missing data examination as a component of the filtration process. Data preprocessing is performed before machine learning analysis, including verifying missing data. Our research resulted in the final sample, which includes 627 worldwide firm data from 2021. Data regarding all publicly traded companies was obtained from Refinitiv Eikon.

Our findings showed that corporate carbon emission performance in global corporations is influenced by ESG performance and total diversity score.

Firms involve in ESG as well as diversity practices to be able to achieve sustainable success. Yet, the forecasting of carbon emissions based on ESG scores and diversity scores remains inadequately established due to conflicting findings and enigmas prevalent in the literature.

Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments.

This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future.

Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential.

In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures.

The significance of this research lies in providing an understanding of how economic conditions, including financial development, informal economic activities and economic uncertainty, influence carbon emissions and tries to offer valuable insights for policymakers to promote sustainable development.

The Panel-ARDL method is employed for a group of 30 developing countries from 1990 to 2018. This study analyzes the data obtained from the World bank, International Monetary Fund and World Uncertainty databases.

Based on the empirical results of the extended model, an increase in GDP and energy intensity is associated with an 83 and 14% increase in carbon emissions, respectively. Conversely, a 1% increase in financial development and economic uncertainty is linked to significant decrease in carbon emissions (about 47 and 23%, respectively). Finally, an increase in the informal economy can lead to a negligible yet significant decrease in carbon emissions. These results reveal that financial development plays an effective role in reducing CO₂ emissions. Moreover, while economic uncertainty and informal economy are among unfavorable economic conditions, they contribute in CO₂ reduction.

Therefore, fostering financial development and addressing economic uncertainty are crucial for mitigating carbon emissions, while the impact of informal economy on emissions, though present, is relatively negligible. Accordingly, policies to control uncertainty and reduce the informal economy should be accompanied by environmental policies to avoid increase in emissions.

The originality of this paper lies in its focus on fundamental changes in the economic environment such as financial development, economic uncertainty, and informal activities as determinants of carbon emissions. This perspective opens up new avenues for understanding the intricate relationship between carbon emissions and economic factors, offering unique insights previously unexplored in the literature.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

This paper originally proposed the fuzzy option pricing method for green bonds. Based on the requirements of arbitrage equilibrium, this paper draws on Merton's corporate bond option pricing model.

Describing the asset value behavior of green bond issuing enterprises through diffusion-jump processes to reflect the uncertainty brought by carbon emission reduction policies and technologies, using approximation methods to get the analytical pricing formula and then, using a fuzzification technique of Choquet expectation under  λ-additive fuzzy measures after considering fuzzy factors, the paper provides fuzzy intervals for the parity coupon rates of green bonds with different subjective levels for investors.

The paper proposes and argues the classical and fuzzy option pricing methods in turn for both corporate ordinary bonds and green bonds, considering carbon risk or climate risk. It implements the scenario analysis varying with industry emission standards and discusses the sensitiveness of the related key parameters of the option.

The fuzzy option pricing for the green bonds provides the scope of the variable equilibrium values, operational theoretical supports and some policy implications of carbon reduction and promoting green funding.

The logic of introducing the fuzziness of the option pricing for the green bonds lies with considering the existence of fuzzy information about the project supported by the green bond and the subjectivity of investors and it also responds to changes in technological uncertainty and policy uncertainty in the process of “carbon peaking and carbon neutrality.”

The gas emissions towards the atmosphere are one of the main and most actual environmental problems in the world. The effects of greenhouse gas emissions have been studied and treated recently in the Climate Change Conference in Kyoto. In the approved Kyoto Protocol, the European Union will reduce emissions by 8 per cent, the USA by 7 per cent, and Japan by 6 per cent. The data for each country are used to implement policies and make global decisions regarding the level of emissions allowed in the future. For this reason, a study more in depth about the origin and level of emissions from a regional perspective becomes necessary, due to the implications on regional development. The study provides detailed information regarding atmospheric emissions in Spanish regions. Shows that in many cases the atmospheric emissions are not directly related to the economic situations of each region. For this reason, environmental policies should pay attention to the regional differences within a country.

This paper aims to report on the results of a case study in Botswana, aimed at raising awareness on climate issues. Higher-education institutions play a leading role in sustainability efforts, as their research role often lays the groundwork for social transformation.

The Clean Air-Cool Planet (CACP) campus calculator was used to calculate emissions from various sections within the college.

Total greenhouse gas (GHG) emissions from the college is 3,432.66 metric tons CO₂e resulting in per capita GHG emissions of 3.20 metric tons CO₂e, which is high compared to other universities. Options for reducing emissions are proposed.

The procedure in carrying out the study provided learners with an opportunity to appreciate emissions from developing countries and also gain technical skills in conducting a GHG inventory. It also sensitized campus administrators about the scale of emissions and possible ways of reducing them.

This paper is original in that it provides campus greenhouse inventory within a developing country, a unique undertaking. Furthermore, it highlights the fact that developing countries also produce significant emissions, hence the need for mitigation measures.

To reach building owners, architects, designers, builders and manufacturers of materials and building components in order to achieve good indoor air quality (IAQ).

The emission classification of building materials is a part of the Classification of Indoor Climate 2000, which is intended to be used in the design and construction of healthier and more comfortable buildings and their mechanical systems in Finland.

Manufacturers have developed new products with lower emissions by using advanced consistency of materials and improved technology in production and production control. Over the years they have improved the quality of their products so that measured harmful emissions have lowered drastically. Similar development has also been seen in the measured sensory emissions of classified materials and products. In January 2006, there were over 900 classified products.

It is necessary to expand the emission classification of building materials because of the IAQ problems that have arisen related to emissions in the last few years. The methodology is intended for national use, but it is possible to implement the system in other countries.

The emission classification of building materials includes target values for odours and emissions of the materials and recommended maximum surface areas of the materials based on their emissions.

This paper fulfils an identified information/resources need. It is thought‐provoking and offers insights for the future planning and developing of classification systems.