Amid the growing prevalence of avoided emissions reporting within corporate ESG disclosures and expanding capital flows toward climate solutions, the proliferation of methodologies for avoided emissions quantification allowing significant discretion raises concerns regarding their comparability. This study examines how methodological flexibility affects avoided emissions estimates, using the Tesla Model 3/Y electric vehicles as a case study. While existing literature highlights methodological variability, it has not quantified the range of potential outcomes arising from these differences. This study addresses that gap by demonstrating that both the application of distinct methodologies and variations within individual guidance frameworks yield highly divergent estimates, ranging from −10 million tons CO₂e to 230 million tons CO₂e of avoided emissions from 2024's production of Tesla Model 3/Y electric vehicles. These findings suggest that avoided emissions disclosures risk lacking comparability and usability, potentially facilitating greenwashing and undermining the effective allocation of capital toward climate solutions. Reflecting on the case study findings and process, this paper recommends the standardization of avoided emissions methodologies, with the development of a formalized disclosure nomenclature as an initial step. Further research is required to support such standardization efforts and to address outstanding methodological challenges.
This study examines whether the adoption of International Financial Reporting Standards (IFRS), International Public Sector Accounting Standards (IPSAS), and Environmental, Social, and Governance (ESG) disclosure is associated with lower CO₂ emissions. Using panel data for developing and transition countries over the period 2011–2023, we find that the adoption of each reporting framework is individually associated with lower CO₂ emissions. More importantly, the simultaneous adoption of all three frameworks is associated with substantially larger emissions reductions, suggesting important complementarities among financial, public-sector, and sustainability reporting reforms. Additional analyses show that these associations are stronger in countries with higher institutional maturity and extend to neighboring countries through regional spillover effects, complementing the broader literature on the diffusion of institutional reforms. The study contributes to the growing literature on transparency and environmental governance by highlighting the complementary role of integrated reporting reforms in developing and transition economies.
During the revision of the GHG Protocol's scope 2 guidance, debates on addressing existing accounting issues have intensified. Prominent market-based accounting proposals include variations of "hourly energy matching" and "carbon matching", but uncertainty around their underlying accounting purposes has caused confusion. To address this, we reviewed more than 250 GHG accounting purposes from the literature and categorized them into 18 thematic groups. Based on this review, we propose a four-step mapping approach to assess the conceptual alignment between scope 2 accounting methods and their stated purposes: (1) define specific purposes using an abstraction hierarchy; (2) link purposes to an accounting framework: attributional (physical), consequential (physical), or performance (non-physical); (3) identify accounting requirements: deliverability and additionality; (4) select a suitable matching method: energy matching or carbon matching. Applying this approach to three method proposals submitted to the 2023 scope 2 revision process, revealed inconsistencies between stated purposes, requirements, and proposed methods, with all proposals aligning with performance accounting despite the stated purposes suggesting otherwise. To improve the GHG Protocol's scope 2 guidance, we recommend either incorporating deliverability and additionality requirements into physical inventory accounting or explicitly labeling it as performance accounting. Without such alignment, misleading claims are likely to persist.
The agricultural sector plays a critical role in national climate strategies, particularly in emerging economies where high emission crops are prevalent. This study evaluates cost-effective greenhouse gas (GHG) mitigation strategies for sugarcane cultivation in Thailand using a scenario-based framework. Eleven mitigation technologies including biofertilizer, solar irrigation, and machine harvesting were assessed through a marginal abatement cost curve (MACC) approach under four policy relevant future scenarios. The results reveal significant variation in abatement potential and cost-effectiveness depending on institutional support, financial incentives, and technology accessibility. The Clear Sky scenario achieves the highest mitigation potential, 5.33 MtCO(2)e per year, with marginal abatement costs ranging from negative 258.47 to 65.27 USD per tCO(2)e. These findings highlight the need for targeted investment and enabling policies to accelerate technology adoption and realize sustainable transitions in agricultural GHG management systems. The methodology provides a transferable decision-support framework for guiding climate-smart agriculture under diverse governance and socio-economic contexts.
Net zero carbon priorities drive a substantial portfolio of research initiatives. As such interest into the ability to assess impact if research outputs were to be implemented at scale is growing. Namely, the potential future avoidance of carbon emissions. This is of interest both to justify net zero research efforts and provide confidence to investors and implementers of the scale of impact. However, current carbon calculation tools do not extend into potential future avoidance. Furthermore, emerging terms and definitions are open to misinterpretation and subsequent legitimacy concerns. This study critically analyses current developments in carbon emission assessments for avoided emissions within research and innovation projects, proposes alternative terminology to overcome legitimacy concerns and introduces the Potential Avoidance of Carbon Emissions through Research (PACER) assessment framework to address this research gap. The PACER framework has been developed through a funded researcher development programme thematically linked to achieving reduced greenhouse gas emissions and reaching net zero carbon targets: the Centre for Post-doctoral Development in Infrastructure Cities and Energy (C-DICE). Case studies used in the development of the project demonstrate the ability of the framework to assess potentially avoided carbon across project development timelines and the potential insights that can be gained.
Transportation electrification is among the vital solutions for green transport environments. As the number of electric cars increases and results in a higher penetration rate, a fast deployment of electric charging stations is needed to fulfill the demand of Electric Vehicles EVs owners concerned about range, increase charger availability and reduce the waiting time to charge. However, deploying a charging infrastructure can be costly and logistically challenging, especially if super-fast chargers need to be deployed. In this research, the problem of assigning a fleet of battery-electric cars to an existing points of interest in the presence of different constraints is addressed. The constraints considered include the number of charging stations, final battery state, waiting time, driving style, and charging cost. To solve the assignment problem, two optimization models are introduced in this work: Mathematical Programming and a Greedy Algorithm to show the utility of both methods, a case study comprising a set of cars requiring charging along a highway was introduced. Both models fulfill the charging needs of all battery electric (BE) cars using three charging stations, resulting in the same deployment cost. However, the total cost differs because of charging revenue differences. Additionally, sensitivity analysis was conducted to assess the impact of model parameters on assignment and total cost.
Against the backdrop of China's "dual-carbon" goals, heavily polluting firms face tightening environmental regulation and stakeholder scrutiny. Meeting these pressures requires not only technological and process upgrading, but also stronger internal governance and internal control systems, which may shape auditors' risk assessments and audit effort. Using A-share listed firms in heavily polluting industries in China from 2013 to 2022, this study examines whether internal control quality affects audit quality and whether analyst attention moderates this relationship. Set against China's dual-carbon transition, the paper focuses on the governance and information environment of environmentally sensitive firms rather than on environmental outcomes directly. The results show that internal control quality is positively associated with audit quality, indicating that stronger controls are linked to higher-quality audits. However, analyst attention weakens (attenuates) this positive effect, suggesting that external information environments can partially substitute for internal governance signals in auditors' judgments. Decomposing internal control into five components reveals that the control environment, risk assessment, and monitoring are significantly positively related to audit quality; control activities show no significant association; and information and communication are negatively related. Further heterogeneity analyses indicate that the positive internal control-audit quality association is more pronounced among state-owned enterprises, firms with lower ownership concentration, and firms operating in more marketized regions. These findings remain robust to the Heckman two-step procedure, alternative measures of key variables, and changes in the sample window, and provide implications for improving audit quality in high-emission sectors.
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 tCO(2)e (95% CI: 245.2-287.8 tCO(2)e), 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 tCO(2)e), strongly correlated with ambient temperature (Pearson's r = 0.71, p < 0.01). Emission intensity of 0.093 tCO(2)e 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 tCO(2)e), 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 carbon emission trading system (CETS), as a market-based mechanism for energy conservation and emission reduction, effectively drives green innovation among enterprises and exerts a significant impact on their financial performance. Using panel data from listed industrial enterprises during the period 2009-2022, this study employs a multi-period difference-in-differences approach, propensity score matching, and a mediation effect model to investigate the impact of carbon emission trading on corporate financial performance. The findings reveal that carbon emission trading significantly improves the financial performance of industrial enterprises. Mechanism analysis demonstrates that the carbon emission trading system enhances corporate financial performance by improving ESG performance and promoting green technological innovation, meanwhile green financing constraints play a negative moderating role in the relationship between carbon emission trading and financial performance. Heterogeneity analysis indicates that while the policy substantially enhances the financial performance of state-owned enterprises, its effect on nonstate-owned enterprises is statistically insignificant. Moreover, both small and medium-sized enterprises (SMEs) and large enterprises benefit from carbon emission trading, with the positive effect being more pronounced for SMEs. Corresponding countermeasures are proposed to advance carbon emission reduction in industrial enterprises from the dimensions of market development, ESG disclosure, innovation incentives, and financing support.
In today's globalized industrial landscape, manuacturing has evolved from an initial phase of free and balanced development into a more distinct pattern characterized by the formation of industrial clusters and network hubs. This study investigates industrial clustering and network hub effects by applying small-world and scale-free network models to model the co-evolutionary dynamics between enterprise strategic behavior and the network topology, using the beer company in the food industry as a representative case. The research findings demonstrate that by establishing carbon footprint and labeling systems, policymakers can build a low-carbon supply chain linking the manufacturing supply side with consumer demand, thereby enabling consumers to effectively identify low-carbon products. More manufacturing companies are being integrated into this network as consumer preferences for low carbon become more evident. To accelerate the diffusion of low-carbon technologies, policymakers should advance structural transformation in the manufacturing sector and strengthen support for small and medium-sized enterprises. Furthermore, scale-free networks exhibit greater resilience to disruptions than small-world networks. Oligopolistic enterprises are less susceptible to external network shocks due to their substantial economic resources. Policymakers should therefore encourage these enterprises to assume a leading role in guiding surrounding small and medium-sized manufacturers toward collective adoption of low-carbon production practices.
Understanding the key drivers of soil organic carbon (SOC) stock changes (Delta SOC) is essential for accurately modelling national greenhouse gas (GHG) inventories and carbon accounting projects. This study introduces a surrogate modelling framework to improve Delta SOC estimation and identify its drivers across Australian cropland and grassland systems. Surrogate models were developed using machine learning, integrating outputs from the Full Carbon Accounting Model (FullCAM), a calibrated process-based model used in Australia's National Greenhouse Accounts. The models showed high agreement with FullCAM predictions, particularly for croplands, which demonstrated higher accuracy and lower bias than grasslands. Key drivers of Delta SOC were identified using Shapley additive explanations, highlighting the importance of land management practices, climate variables, and initial SOC stocks. Dynamic drivers, including time-discounted biomass and cumulative precipitation, emphasised the influence of historical conditions on SOC variability. Compared to process-based models, surrogate models have lower computational demands, enabling scalable applications for national and regional SOC assessments. While limitations remain, particularly due to reliance on FullCAM outputs, the framework provides a practical and efficient approach for Delta SOC estimation. It also demonstrates strong potential to integrate mechanistic and machine-learning models to support landscape-scale GHG accounting and sustainable land management.
The energy policy of the European Union aims to achieve carbon-neutral and sustainable energy while preserving the quality of life. Using two case studies from the EU country Czechia, this study develops a general procedure for evaluating the inefficiency of selected environmental investments aimed at renewable energy production. First, the economic and environmental benefits of photothermal and photovoltaic solar collectors were compared. The result showed the necessity of planning the share of both types of collectors due to the risk of inefficiencies in accordance with the form of energy demanded. For an area of 60 m2, the difference in annual energy yield can reach up to 35.4 MWh. Secondly, the potential risks of economic and environmental inefficiencies in the production of electricity from biomass through a biogas plant were researched. In many cases, unused energy production is a source of economic and environmental inefficiency, which can represent savings of up to & euro; 1000,000 and more than 2000 tons of CO2 per year for a typical biogas plant. The identified inefficiencies are also contrary to the European Commission's efficiency requirements, which poses a challenge, especially for national public policy makers.
This study explores the spatiotemporal patterns of construction carbon emissions in Central China (Anhui, Shanxi, Jiangxi, Henan, Hunan, Hubei) under China's carbon peak and neutrality goals. Using certified construction data, NPP-VIIRS nighttime light data, energy statistics, and socioeconomic panel data from 2012 to 2025, we examine emission dynamics and spatial heterogeneity. Results show that total emissions reached 1.039 billion tons of CO2 equivalent, with Shanxi accounting for over 20%. Spatial clustering exhibited a fluctuating downward trend, with High-High clusters in Shanxi and northern Henan, and Low-Low clusters in southern Anhui, western Hubei, and northern Jiangxi. Geodetector results reveal that regional GDP and secondary industry output were dominant drivers, and their interactions with population and technology investment reached a maximum q-statistic of 0.98. These findings support targeted low-carbon policies for the construction sector in transitional regions.
This paper aims to develop and resolve a tension between two motivations for GHG accounting. On the one hand, we want to measure and ultimately reduce our emissions because climate change causes damage, as spelled out in the IPCC's Reasons for Concern framework. On the other hand, international agreements (such as the Paris Agreement) propose to limit warming to a particular level, and we use GHG accounting to measure our progress towards net zero goals set with these limits in mind. We show that these two motivations impose incompatible constraints on our choice of GHG accounting methods and metrics. We then develop a novel dual ledger approach, which incorporates two different metrics—one focused on stabilisation temperature and the other on damage. We argue that progress toward net zero goals should be measured using a metric focused on stabilisation temperature, but that too narrow a focus on stabilisation temperature will lead us to miss other opportunities to reduce climate damage. Since it is important to make use of all the ways we have to reduce damage, we suggest that decision makers also need metrics focused on damage.
Using a staggered difference-in-differences (DID) approach on a sample of Chinese A-share listed firms, we find that the carbon emissions trading system (ETS) increases financial distress risk for emission-regulated firms, prompting them to adopt more conservative financial strategies. To mitigate the risks of carbon reduction policies, these firms reduce total investment, increase cash holdings, and reallocate resources toward low-carbon technologies to facilitate the low-carbon transition. Our analysis of heterogeneity in market competition reveals that the inhibitory effect of the carbon emissions trading system on corporate investment is stronger in less competitive markets. This study shows that climate policy risk really does play a role in how companies make financial decisions. It also provides insights for policymakers on designing cost-bearing mechanisms within climate mitigation policies to ensure the effective implementation of the “polluter pays” principle.
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 CO(2)e/GB, significantly higher than terrestrial systems (0.05-0.20 kg CO(2)e/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 (beta = 0.1435, p < 0.01), while the share of renewable energy reduces emissions (beta = -95.1288, p < 0.05), with LEO penetration having only a minimal impact (beta = 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.
Traditional gold mining (TGM) poses a critical threat to soil quality and carbon storage in Africa. This study investigates how TGM alters soil organic carbon (SOC), plant biomass carbon (PBC), bulk density (BD), and soil coarse fragments (CF) across slope positions, land-use/land-cover types, and soil depths in Tigray, Ethiopia. A total of 384 (192 composite and 192 undisturbed) soil samples were collected from mined and unmined counterparts, along with nested vegetation plots to estimate PBC. Data were analysed using generalized linear mixed models, correlation, and mediation analyses. Results show that TGM significantly reduces SOC stocks by 75.9% in upper-slope woodlands while significantly increasing BD and CF accumulation. The highest SOC (53.22 tons ha(-1)) was observed in unmined woodland foot-slope, whereas the lowest (3.48 tons ha(-1)) occurred in upper-slope mined farmlands. Mediation analysis revealed that 74% of SOC loss was driven by TGM-induced CF accumulation. Total organic carbon was also lower (p < 0.001) in mined woodlands (16.77 tons ha(-1)) than in unmined (86.72 tons ha(-1)) counterparts. The results demonstrate that TGM is a major driver of organic carbon depletion across dryland-ecosystems. Post-mining restoration measures, including backfilling of mined pits following natural soil horizon sequences and revegetation with native dryland species, are recommended to restore carbon functions.
Greenhouse gas emissions from anthropogenic activities, especially CO2, are the primary cause of global warming. Accurate estimation of urban-scale anthropogenic carbon emissions is critical for developing emission reduction policies and achieving carbon neutrality. This study focuses on the top-down inversion of anthropogenic CO2 emissions in the Chengdu-Chongqing Economic Circle (CCEC) from December 2019 to May 2020, using a coupled Weather Research and Forecasting (WRF) model and Stochastic Time-Inverted Lagrangian Transport (STILT) model. The model integrates EDGAR and GRACED prior carbon emission inventories with CO2 concentration observations and applies a multi-ratio factor Bayesian optimization algorithm to invert sectoral carbon emissions. Results show clear temporal and spatial variations in footprint weights, and the WRF-STILT model effectively simulates CO2 concentrations at hourly and daily scales. Simulations based on GRACED are closer to observed values than those from EDGAR, with enhancements ranging from 8 to 47 ppm. The industrial sector contributes most to CO2 increases, followed by the power sector. CO2 concentrations from GRACED show a correlation of over 0.94 with observations, indicating strong tolerance to concentration errors. The WRF-STILT model enables accurate sectoral emission inversion with a small constraint (+/- 2 ppm) on atmospheric CO2 concentrations.
There is broad recognition that removals of carbon dioxide and other greenhouse gases (GHGs) will be required for counter-balancing emissions from hard-to-abate sectors to achieve net zero, and will also be necessary in the increasingly likely event of an emissions overshoot in order to return atmospheric concentrations of GHGs and temperature change below target levels. However, the term “removal” is open to imprecise usage, which is likely to result in confusion over which activities policy-makers, investors and carbon credit buyers wish to support, and what project developers and technology providers actually deliver. This commentary paper aims to clarify what is meant by “removals” by analysing the definitions from prominent sources and by proposing a precise definition of the term. The paper also identifies a further source of confusion, which is that “removals” is often used as a shorthand to refer to removal-related activities that fulfil specific characteristics. Rather than adjudicate on shorthand linguistic conventions we use two distinctions to create a four-way classification, which can be used to refer precisely to different types of removal-related activities. We offer some remarks on the relative mitigation value of different types of removal-related activities, noting that ultimately all actions that achieve net removals or emission reductions help to address climate change.
The Conference of the Parties (COP) prioritises global greenhouse gas (GHG) emission mitigation, yet rising temperatures and intensifying climate impacts highlight persistent implementation gaps. This study quantifies global patterns and drivers of GHG emissions across major emitting regions from 1990 to 2020, including CO2, CH4, N2O, and fluorinated gases (HFCs, PFCs, SF6, and NF3). PCA and LMDI decomposition were applied to assess national climate pledges and projected CO2-equivalent pathways to 2100, using gas-specific Global Warming Potentials (GWPs). Coefficient of variation and Pearson correlation analyses reveal a strong positive relationship between emissions and GDP, indicating that economic growth remains closely tied to GHG pathways. Results indicate that CO2 remains the dominant contributor in 2020, followed by CH4 and N2O, whose higher GWPs increase long-term warming impacts, with China, the United States, and India emerging as leading emitters. The LMDI analysis reveals that the decline in COVID-19-related emissions from 2019 to 2020 was transient, with marginal shifts in energy and carbon intensity, consistent with the rapid rebound in 2022. Long-term pledge projections to 2100 indicate a persistent implementation deficit; despite widespread net-zero targets, projected emissions remain approximately 20-30% above 1.5 degrees C consistent pathway levels. Sensitivity analysis reveals that robust effects on GDP are achieved under uncertain perturbations, reinforcing the persistence of the growth-emissions nexus and the importance of collaborative, data-driven global efforts to mitigate climate impacts.
