This case report presents the first documented greenhouse gas (GHG) emissions inventory for a non-technical academic faculty in Thailand, addressing a critical gap in institutional carbon accounting where humanities-oriented units have remained systematically underrepresented. The Faculty of Liberal Arts at Rajamangala University of Technology Thanyaburi (RMUTT) was assessed over 12 months under ISO 14064-1 and the GHG Protocol, covering Scope 1 and Scope 2 emissions. Annual emissions totalled 266.5 ± 21.3 tCO₂e (95% CI: 245.2–287.8 tCO₂e), with Scope 2 electricity accounting for 79.7% of the footprint. Among Scope 1 sources, refrigerant leakage—particularly R22—dominated direct emissions owing to its high global warming potential. Monthly emissions exhibited marked seasonal variation (15.43–37.36 tCO₂e), strongly correlated with ambient temperature (Pearson's r = 0.71, p < 0.01). Emission intensity of 0.093 tCO₂e per capita was 70–94% below technical faculty counterparts, confirming that non-technical faculties occupy a distinctly low-emission institutional tier. Monte Carlo simulation (10,000 iterations) yielded total uncertainty of ± 8.0% (95% CI: 224.8–308.2 tCO₂e), with electricity consumption and R22 leakage as dominant variance sources. The findings establish a replicable, ISO 14064-1-compliant framework for faculty-level GHG accounting, directly supporting carbon neutrality planning across Thai and regional higher education institutions.

The rapid expansion of low Earth orbit (LEO) satellite networks to improve global internet access has raised concerns about their environmental impact compared to terrestrial infrastructure. This study conducts a panel lifecycle assessment (LCA) and econometric analysis to compare the carbon intensity of LEO satellite and terrestrial fibre-optic networks across 100 countries from 2005 to 2023. Using ISO 14040-compliant LCA, we find LEO systems emit 0.1–0.5 kg CO₂e/GB, significantly higher than terrestrial systems (0.05–0.20 kg CO₂e/GB), with these emissions generated primarily during the launch (40%) and operational (35%) phases. Fixed-effects regression reveals that internet usage increases CO₂ emissions per capita (β = 0.1435, p < 0.01), while the share of renewable energy reduces emissions (β = –95.1288, p < 0.05), with LEO penetration having only a minimal impact (β = 0.0012, p = 0.332). Advanced two-stage least squares, Arellano-Bond generalized method of moments, Moran’s I (I = 0.12, p = 0.08), and random forest analyses confirm these findings, highlighting renewables and internet usage as key drivers of emissions. The findings suggest that emissions reporting frameworks and renewable energy integration may play an important role in aligning digital connectivity expansion with climate goals. These results highlight the need for sustainable digital infrastructure to balance global connectivity with planetary boundaries.

This paper is among the first studies to assess the role of the carbon footprint of bank loans in the stability of the global banking system. Employing a Bayesian approach on a sample of 36 countries with the carbon footprint of bank loan data from 2005 to 2018, the results reveal that increased lending to high-emission sectors enhances bank stability. This predicts a difficult transition of economies to a low-carbon state under the Paris Agreement 2015. Countries need a long-term but clear roadmap, along with support for the banking system, to gradually shift bank lending from brown industries to green industries without affecting the stability of banks. In addition, banks need to increase the size of their operating capital and a higher level of concentration to control transition risks, towards a more stable and secure banking system in a low-carbon environment.

ABSTRACT Generative artificial intelligence (GAI), as a breakthrough in the field of AI in recent years, is reshaping the production mode and creativity boundary of human society in an unprecedented way. This paper examines 12,114 SCI/SSCI documents included in Web of Science from 1988 to 2024 as the research object. It uses CiteSpace software to explore the development lineage, research hotspots, empowerment paths, and future trends of GAI in the field of energy economy research in terms of temporal distribution, institutional distribution, keyword co-occurrence, and theme clustering. The research results show that: (1) In terms of the timeline perspective, GAI-empowered energy economy research as a whole shows a fluctuating upward trend, beginning to sprout in 1988, entering a period of development with steady growth from 2009, and showing a boom period of explosive growth from 2017 to the present. (2) In terms of spatial distribution, the distribution of countries and regions, and research institutes shows a multipolar trend; and the inclusion of journals shows a trend of thematic refinement, diversification, and interdisciplinary integration. (3) In terms of research hotspots, “renewable energy development” and “utilization”, “model construction”, and “system optimization” are the core research content in this field. Among them, the terms “model”, “optimization”, “design”, and “renewable energy sources” are the core buzzwords about the research in the field of GAI-empowered energy economy; “carbon neutrality” becomes the keyword with the highest surge intensity in 2022−2024. (4) In terms of empowering paths, GAI has empowered energy economy through capabilities ranging from high-precision data generation, material design discovery, and complex model construction, multimodal data fusion, strategy simulation and scenario generation and optimization, and fault diagnosis and warning. (5) In terms of future trends, energy economics research topics ranging from early energy shortages and environmental issues to the discovery and use of renewable energy sources and model optimization, and in the future, it will be deeply engaged in deep-learning artificial intelligence technologies and hotspots such as carbon neutrality and energy efficiency and other hotspots.

Carbon balances of croplands are often assessed using models that depend critically on accurate estimates of soil carbon inputs such as crop residues, dead roots, and root exudates. Here, we develop a method to estimate soil carbon inputs by combining satellite-based gross primary productivity (GPP) estimates with harvest yields extracted from agricultural statistics. We model the daily GPP as a statistical regression on photosynthetically active radiation and the red edge chlorophyll index measured by the Sentinel-2 satellites and train the model using data from five eddy covariance flux measurement sites. When tested with leave-one-site-out cross-validation, the model predicted the yearly GPP with a root mean squared error equal to about 10% of the mean. We furthermore show that the predicted cumulative GPP explains 60%–70% of the observed variability within a set of 135 above-ground biomass measurements collected on 40 agricultural fields. Finally, we apply the method to three Finnish regions and estimate the soil carbon inputs as the difference between the net primary productivity (NPP), assumed 50% of the GPP, and the carbon removed in harvest. Compared to the allometric method used in the current national greenhouse gas inventory, our annual carbon inputs are within 5%–30% for wheat but 50%–100% higher for barley, 40%–50% higher for green fallows, and 100%–160% higher for forage grasses. By highlighting a discrepancy between the current national greenhouse gas inventory and carbon budgets derived from eddy covariance measurements, these results provide a path towards closer integration of Earth observation and flux measurement data into greenhouse gas inventories and reporting systems.

Enacting and implementing effective regulations for carbon emissions in the built environment is imperative in order to reduce emissions and thus achieve a net-zero built environment. While various technologies such as BIM, IoT, RFID, and Blockchain hold the potential to enable the successful enactment and implementation of emissions regulations, integrating their capabilities with regulatory processes has presented challenges. To bridge this gap, we conducted a systematic literature review focusing on carbon emissions regulations and emissions management technologies. The review uncovered two key aspects: firstly, the essential requirements for successfully enacting and implementing carbon emission regulations in the built environment, and secondly, the potential of technologies like BIM, IoT, RFID, and Blockchain to facilitate and support the identified prerequisites for successful regulation enactment and implementation. By integrating the findings from these two bodies of literature, we have developed a comprehensive framework for technology-enabled carbon emissions regulations. This framework offers valuable insights for policymakers and regulators, providing guidance on leveraging various technologies to effectively enact and implement carbon emissions regulations. Furthermore, it establishes a sturdy theoretical foundation for future empirical research on technology-enabled carbon emissions regulations by elucidating the intricate interplay between the prerequisites for successful regulation enactment and implementation and the application of diverse technologies.

In greenhouse gas (GHG) inventories of companies and products emissions of purchased energy, especially electricity, make up a significant proportion. Therefore, companies are interested in sourcing and accounting for green electricity with a low emission factor. The market- and location-based approaches are two different accounting methods leading to different results. If companies base green claims on the method that produces the result most beneficial to their GHG inventory, this can lead to double counting of renewable energy attributes. This harbors the risk of overestimating the amount of energy sourced from renewable energy sources, and hence, the associated positive environmental impacts of companies’ renewable energy consumption. Moreover, it limits the comparability of companies’ GHG inventories, reduces transparency and credibility of GHG reporting and associated green claims. In this commentary, we outline the problem of leaving a de facto choice between location- and market-based accounting approaches for emissions from electricity procurement when making green claims. To achieve credible green claims for customers and a level playing field for companies, we suggest a consistent application of the market-based approach. We propose introducing a requirement to base green claims on this method, if a market region allows for the use of contractual instruments meeting standardized quality criteria.

China’s “14th Five-Year Plan“ sets ambitious goals to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. Since 2013, China has been piloting carbon emission trading policies to internalize environmental externalities through market mechanisms. This paper employs a quasi-natural experiment, using data from Chinese companies spanning 2010 to 2021, to analyze the impact of the pilot carbon emission trading policy on green technology innovation and financial performance, utilizing a difference-in-differences approach. The findings indicate that: (1) the carbon emission trading policy positively affects firms’ financial performance, with long-term benefits; (2) while the policy initially suppresses financial performance by driving green technology innovation, this negative effect diminishes over time; (3) the long-term effects are particularly significant, with heterogeneity analysis revealing more pronounced impacts on state-owned enterprises and firms located in high-carbon price regions. These insights are crucial for understanding the policy’s role in promoting sustainable corporate development and guiding the ongoing refinement of China’s national carbon emissions trading market.

Reducing greenhouse gas emissions from economic activity is a growing priority for companies globally in support of corporate net-zero targets. Scope 3 emissions—those occurring throughout a company’s value chain—are not only the largest share of most companies’ carbon liability, but also the most difficult to address. The prescribed approach relies on “supplier engagement,” a catch-all term that includes everything from moral suasion to economic subsidy. However, this often fails to extend beyond first- and second-tier suppliers. This leaves the bulk of upstream emissions—generated by deep-tier, hard-to-abate commodity producers—unaddressed. Environmental Attribute Certificates (EACs) are market-based instruments representing verified environmental attributes (e.g., the quantity of GHG emissions per unit of production) linked to specific upstream commodities. We propose that by enabling downstream firms to purchase these certificates and claim verified decarbonization benefits independent of physical supply chain flows, EACs can support an insetting regime that bridges supply chain distances and provides a scalable pathway to Scope 3 decarbonization. Whether through bilateral deals or inclusive market platforms, EACs can offer demand guarantees and price stability to producers of hard-to-abate commodities, supporting investment in low-carbon technologies. Critical implementation issues such as responsibility accounting, double counting, and frameworks to ensure credibility are also acknowledged.

As the urgency of climate change grows, higher education institutions are increasingly accounting for, and reporting, their annual greenhouse gas (GHG) emissions. An often-overlooked source of GHG emissions are those associated with loans and investments. The purpose of this work is to quantify financed emissions from investment holdings in a public research university endowment, using Northern Arizona University (NAU) as a case study. The methodology for calculating financed emissions is drawn from the Partnership for Carbon Accounting Financials (PCAF), which describes how to attribute GHG emissions from investee companies to the investor on an annual basis. Financed emissions from NAU’s endowment are estimated to be a substantial component of NAU’s GHG inventory, highlighting the significance of this emissions category for public research universities with endowments. The results are presented for individual investee companies and discussed as an indicator of climate-related financial risk. Further, the results show that the divestment of fossil fuel companies from NAU’s endowment would lead to a 22% reduction in financed emissions.