ABSTRACT Against the backdrop of advancing the United Nations sustainable development goals SDG 7 (affordable and clean energy) and 13 (climate action), measuring and reducing carbon emissions has been critical for ports’ decarbonization and sustainable development. This paper integrates data from multiple sources to establish a comprehensive carbon emission evaluation framework for ports, employing system dynamic simulation models to analyze the impact of various emission reduction strategies. And a dual modeling approach integrates bottom-up and top-down methodologies to respectively estimate emissions from in-port ships and port facilities, predicting forward emissions for multiple scenarios using artificial neural networks. A case study of the world’s busiest container port—Shanghai Port is presented to demonstrate the proposed evaluation framework. The result shows that strategies, such as adopting alternative fuels, implementing government subsidies, and promoting carbon trading schemes, are effective methods for reducing carbon emissions. This study develops a data-driven framework to identify the most effective emission-reduction strategies providing a foundation for port managers and operators to assess and improve the sustainability performance of their operations. The results also offer insights for policymakers to reduce port carbon emissions and enhance sustainable port development.

Meeting global climate targets requires cost-effective strategies for reducing carbon emissions, especially in the waste management sector. Waste-to-energy (WtE) technologies are widely recognized for their greenhouse gas mitigation potential by diverting waste from disposal sites and displacing fossil fuels with energy recovered from waste. However, their cost-effectiveness for carbon abatement remains underexplored. This study introduces the Levelized Cost of Carbon Mitigation (LCOCM) as a novel metric to assess the economic feasibility of WtE technologies in Iran. The LCOCM is particularly valuable in countries where carbon pricing mechanisms such as emission trading schemes (ETS) are not yet in place, thereby serving as an alternative decision-support tool for policymakers. The results show LCOCM values of $32/ton CO₂-eq for incineration, $14/ton CO₂-eq for anaerobic digestion (AD), and $7/ton CO₂-eq for landfill gas (LFG) recovery. Financial indicators confirm that LFG and AD are economically viable options for carbon-emission reduction, with NPVs of $1.9 billion and $1.1 billion, IRRs of 28 % and 13 %, and payback periods of 3 and 7 years, respectively, whereas incineration is not viable under current conditions. Sensitivity analyses further underscore the critical role of operational optimization—such as extended lifespans, improved energy yields, and minimized downtimes—in reducing the LCOCM. This study demonstrates the cost-effectiveness of WtE technologies for carbon mitigation and provides a replicable framework for sustainable energy and climate policy in emerging economies. • Introduces LCOCM as a novel metric for assessing WtE carbon mitigation costs. • AD and LFG show strong economic feasibility with positive NPV and high IRR. • Incineration offers highest mitigation but remains financially unviable. • WtE systems could achieve up to 54 % of Iran’s 2030 emission reduction target. • Sensitivity analysis identifies CAPEX, OPEX, and efficiency as key cost drivers.

Emission trading scheme reshapes the decarbonisation pathways of China's power sector

A trustworthy vehicle-to-vehicle power trading scheme based on spatio temporal network and blockchain

ABSTRACT Green innovation is a powerful tool for enhancing sustainability in organizations by focusing on environmental, social, and economic perspectives. Despite the benefits of green innovation, its implementation remains abysmal in developing countries. While previous research has identified various policies, it has often overlooked their combined impact hindering the successful adoption of green innovation. To address this lacuna, the current study empirically identified policies to promote green innovation adoption in an emerging economy by using the Pakistani manufacturing sector as a case. A unique approach integrating the fuzzy Delphi method (FDM), interpretive structural modeling (ISM), and cross‐impact matrix multiplication applied to classification (MICMAC) was developed to analyze the policies. First, green innovation policies were identified through an extensive literature review; they were then filtered using the Delphi method. Second, the ISM approach was used to meticulously adjudicate interactions between the identified policies. Finally, MICMAC was used to determine the driving and dependence power of policies. The study's findings indicate that “rules and regulations” and “green taxes and emission trading scheme” are the most significant policies for green innovation adoption while “big vision” and “corporate social responsibility” are the least important. The practitioners and manufacturing industry managers can promote green innovation initiatives by incorporating rules and regulations and an emission trading scheme.

Emissions Trading Scheme (ETS) pilot programs impose binding quota constraints and enable allowance trading, reshaping cost structures and strategic interactions in oligopolistic agri-food supply chains. This paper quantifies the resulting economic impacts, including equilibrium prices, profits, and trade flows, by developing a multi-tier network equilibrium model that links upstream suppliers, downstream manufacturers, domestic and international demand markets, and a carbon trading center under an Emissions Trading Scheme pilot setting. Suppliers invest in low-carbon technologies, while manufacturers undertake labor-efficiency investments that affect unit costs and throughput, with proximity-based spillovers captured via a grid-distance mechanism. The equilibrium conditions are formulated as a variational inequality framework and computed numerically, enabling systematic comparative statics analysis under alternative quota stringency and trading conditions. Using China-EU garlic trade as an illustrative case, the numerical analysis indicates that tighter policy constraints and trading conditions shift production and allowance-trading patterns, with corresponding changes in prices, profits, and emissions across tiers. It also shows that moderate efficiency investment can improve productivity and may reduce aggregate emissions, whereas very high unilateral investment tends to exhibit diminishing returns and can be associated with non-smooth adjustments in network allocations. Finally, coordinated upstream-downstream investment is generally associated with more stable outcomes than isolated initiatives. The framework offers a decision-relevant tool for evaluating Emissions Trading Scheme pilot designs in regulated international agri-food trade networks.

ABSTRACT This study examines the impact of the Emission Trading System (ETS) on firms’ cash flow volatility, using China’s ETS pilot as a quasi-natural experiment. We employ difference-in-differences regressions on a sample of 3,714 listed Chinese firms in high carbon emission industries. The results show that the ETS significantly increases firms’ cash flow volatility. However, this effect is heterogeneous across cohorts: firms in the 2013 pilot regions (Guangdong, Shanghai, Tianjin, and Beijing) exhibit no significant change, whereas those in the 2014 (Hubei and Chongqing) and 2016 (Fujian) cohorts experience significant, delayed, and positive effects. Further analyses indicate that research and development expenditure, environmental, social, and governance (ESG) performance, and board gender diversity (female on board) significantly and negatively moderate this relationship. Overall, the findings suggest that sustainability practices and gender diversity can help mitigate ETS-induced cash flow fluctuations, thereby providing important implications for policymakers and corporate managers.

Can market-based environmental regulation drive a green breakthrough in industrial transfer hubs? Evidence from China’s Carbon Emission Trading Scheme

[Purpose] This study investigates whether firms’ greenhouse gas (GHG) emissions reductionactivities and emissions trading outcomes under the Emissions Trading Scheme (ETS) areassociated with the cost of equity capital. [Methodology] We measure GHG emissions reduction as the difference between a firm’sETS quota and verified emissions, and estimate the implied cost of equity capital usingvaluation－based models. We also examine whether firm－level emissions trading positions arerelated to ICC. The sample covers ETS firms over 2015－2020. [Findings] We find that greater GHG emissions reduction is associated with a lower ICC. In addition, an emission surplus is negatively related to ICC, whereas an emission shortage ispositively related to ICC. Cross－sectional tests further indicate that the negative associationbetween emissions reduction and ICC is more pronounced for firms subject to heightened carboninformationdemand (e.g., CDP－related firms) and for firms exhibiting stronger operating activity. [Implications] Our evidence suggests that carbon－management performance under the ETSis priced in equity financing costs. The findings provide implications for firms’ carbon strategiesand for policy makers designing quota allocation and market mechanisms by highlighting thelink between emissions management and equity capital costs.

The electrical characteristics of distribution networks (DNs) are drastically changing, which is mainly due to widespread adoption of small-scale distributed energy resources (DERs) by end-users. In these cases, conventional planning models may lead to overinvestment choices. This paper presents a planning model for utility companies that explicitly incorporates a model of end-users’ energy-related decisions, considering a neighborhood energy trading scheme (NETS). The model is formulated based on the Stackelberg game (SG) approach, which guarantees the optimality of the final solution for each user and the utility. The proposed mixed-integer second-order cone programming (MISOCP) problem finds the optimal investment plan for transformers, lines, distributed generators (DGs), and energy storage systems (ESSs) for the utility, considering the scenarios of end-users’ investments in rooftop photovoltaic (PV) and battery systems that maximize their benefits. Additionally, a dynamic network charge (NC) scheme is designed to rationalize the network use. Also, Benders decomposition (BD) is used to improve the convergence of the solution algorithm. The numerical studies on a real 23-bus low voltage (LV) network in Perth, Australia, using real-world data reveals that the proposed planning model offers the lowest total cost and the highest penetration of DERs in comparison with conventional models.

ABSTRACT As global concerns over environmental protection and carbon reduction intensify, firms face growing pressure to improve environmental, social, and governance (ESG) performance to maintain legitimacy. Although the ESG‐performance relationship has been widely studied, prior work has focused on net effects, overlooking its resource interdependencies. Drawing on the resource‐based view (RBV), this study applies qualitative comparative analysis (QCA) and constructs ESG scores using machine learning techniques. The results show that high ESG is associated with high firm performance, particularly when coupled with high independent directors and R&D investment. Notably, we identify a complementary relationship between ESG and sales growth, underscoring the interdependence of financial and non‐financial reputations. Pillar‐level analyses underscore the predominant roles of the social and governance dimensions in influencing performance. Finally, heterogeneity analyses further demonstrate that the positive ESG‐performance association occurs more in firms with superior green innovation, companies in sectors with lower market competition, and those in regions without the Carbon Emissions Trading Scheme. Our findings help reconcile previous conflicting findings and provide valuable guidance for sustainable practices.

Abstract Ireland’s climate legislation mandates greenhouse gas reductions consistent with the Paris Agreement, implemented through legally binding carbon budgets (CBs) targeting a 51% reduction by 2030, relative to 2018, and climate neutrality by 2050. As an EU Member State, Ireland must also meet obligations under European climate and energy legislation, including the Emissions Trading Scheme (ETS), the Energy Efficiency Directive (EED), and the Effort Sharing Regulation (ESR). The extent to which national policy frameworks, such as Ireland’s domestic CBs, align with EU obligations is underexplored. This study assesses the alignment of Ireland’s energy system decarbonisation pathways—developed using the TIMES-Ireland model (TIM) and aligned with approved and adopted national CBs—with EU climate and energy targets for 2030 and 2040. The analysis focuses on a composite ‘ TIM-CBaligned ’ pathway, representing the weighted average of scenarios underpinning Ireland’s third and fourth CB proposals, alongside current and planned policy scenarios. Results show that TIM-CBaligned outperforms the 2030 EU ETS target in power and industry sectors by 24% and exceeds the indicative EU-2040 benchmark for energy emissions by 68%. ESR compliance is achievable only with significant agricultural mitigation; otherwise, non-compliance persists even with use of flexibilities. Final energy consumption in 2030 falls 6% short of the EED target, although low energy demand scenarios help to close the gap. These findings confirm that ambitious, CB-aligned energy pathways can deliver strong coherence between national and EU climate goals, in line with literature on multilevel climate governance. However, they also highlight the persistent risk that underperformance in non-energy sectors undermines overall compliance, which is particularly pertinent for countries with a high share of emissions from agriculture. Policy coherence requires sustained investment, accelerated demand reduction, and integrated planning across all sectors. This study contributes a novel, quantitative example of national–EU target alignment, addressing a recognised gap in the literature and providing evidence to inform both domestic and EU policy debates.

ABSTRACT In this paper, we investigate the adoption of carbon pricing policies within the U.S. across states. While policymakers have many tools at their disposal, carbon pricing is a policy option that utilizes market theories to curtail harmful effects from emissions through pricing structures. This is important because as global warming issues become more evident, policy efforts will be needed to intervene. If we can understand what factors drive adoption and the dynamics involved, there is a potential to incentivize policy adoption. In this study, we first examine the factors that drive carbon pricing policy adoption in US states. Then, in response to the findings from the first model, we determine the factors that are driving carbon emissions to identify the overlap in variables that increase the likelihood of carbon pricing policy adoption and decrease carbon emissions. Examining state data from 2005 to 2020, we find that a multitude of factors influence interstate carbon pricing policy adoption, including the political landscape of the state, and the presence of interest groups to name a few. Some overlap is shared between increased likelihood of carbon pricing policy adoption and reduced carbon emissions, therefore it would be beneficial for policymakers and environmentalists to collaborate and potentially incentivize adoption. Related Articles Hedegaard, T. F., and K. Kongshøj. 2024. “Carbon Tax Revenues and How to Spend Them: Danes' Attitudes Toward Revenue Recycling.” Politics & Policy 52, no. 5: 992–1012. https://doi.org/10.1111/polp.12619 . Joo, J., J. Paavola, and J. Van Alstine. 2023. “The Divergence of South Korea's Emissions Trading Scheme (ETS) from the EU ETS: An Institutional Complementarity View.” Politics & Policy 51, no. 6: 1155–1173. https://doi.org/10.1111/polp.12566 . Asadnabizadeh, M. 2024. “Did the Glasgow COP26 Negotiations Meet or Miss Article 6 (Carbon Markets) of the Paris Agreement? A Systematic Review of the Literature.” Politics & Policy 52, no. 4: 757–777. https://doi.org/10.1111/polp.12621 .

The transport sector, particularly aviation and maritime, significantly contributes to climate change, indicating a need for targeted policy actions. The European Union (EU) leads with its 2005 Emissions Trading Scheme (ETS)—the world’s first major international ETS—incorporating aviation in 2012 and maritime in 2024, alongside unique legally binding green fuel mandates. This paper compares EU policies towards aviation and maritime sectors to draw lessons for other regions: (1) expand ETS coverage for greater effectiveness and efficiency; (2) allocate free allowances to reduce stakeholder resistance; (3) extend to interregional connections; and (4) measure and address non-carbon impacts.

Transitioning from Energy Saving Certificates to Carbon Credits: Evidence from India’s Integrated Steel Industry

From grey to green: The role of emission trading schemes in China's urban transformation

This paper examines the price discovery process in the European Union Emission Trading Scheme (EU-ETS) – the largest carbon market across the world – for its third and fourth commitment periods. In particular, we examine the two leading carbon exchanges: European Energy Exchange (EEX: Spot and Futures) and European Climate Exchange (ECX: Futures). We examine the information transmission process in the EU-ETS for the three pairs, namely, (I) EEX spot-EEX futures, (II) EEX futures-ECX futures, and (III) EEX spot-ECX futures. To this end, we employ all three pair-wise bivariate vector error correction models (VECM) and price discovery measures, that is, component share (CS), information share (IS), and information leadership share (ILS) measures. We show that all three-price series substantially contribute to the price discovery. Moreover, the speed of adjustment and price discovery is comparable to the developed equity markets. The ability of carbon prices to incorporate the risk-premia related to climate-risk considerably depends on the pricing efficiency of carbon – one of the major objectives of the Kyoto Protocol and EU-ETS. Thus, these results have significant implications for policymakers, regulators, and academics in the forthcoming carbon markets from emerging economies (e.g., China, India).

ABSTRACT In recent years, the growth and popularity of electric vehicles (EVs) has soared owing to the facilitation of zero‐emission carbon for people commuting on the road, preserving the environment from air pollution and hazardous gases. However, uncertain EV energy demands and their dynamic arrival times impact the ancillary operations and stability of the charging station (CS). Thus, it becomes a challenging task to schedule EVs for charging with their dynamic charging prices, traveling time, and waiting time efficiently and optimally. Thus, we propose an optimal EV selection scheme for trustworthy charging by implementing the hybrid game theory. The hybrid game theory is bifurcated into stage 1 and stage 2, in which stage 1 includes a coalition game to generate EV clusters or coalitions based on the parameters of state‐of‐charge (SoC), energy demand, and penalty factor. Then, the trust values are determined to select the EV pair fairly. Furthermore, stage 2 highlights the zero‐sum game theory, which aims to optimize the payoff at saddle point and formulate strategies for EV pair (generated in stage 1), ensuring the optimal EV selection for trustworthy charging. Moreover, we have utilized the blockchain network to secure the EV optimal payoff by implementing smart contract in Remix Integrated Development Environment (IDE). The hybrid game theory ensures the optimal and efficient EV selection using coalition game to select EV pair then apply zero‐sum game to optimize the payoff at saddle point condition. Next, we implement the hybrid game theory in Python 3.9 to simulate the results with the help of various factors such as trust value comparison, profit comparison based on strategies, convergence comparison, and profit comparison with the traditional approach.

The Unified Payments Interface (UPI) now drives India's digital economy. In FY 2023-24 alone, it processed over 131 billion transactions. However, this massive growth has created new opportunities for financial cybercrime. Banks traditionally rely on rule-based engines or standard machine learning models to stop fraud. These older systems are struggling. They treat transactions as isolated events and miss the hidden connections between accounts. Fraudsters use this blind spot to build money mule networks and circular trading schemes. To solve this problem, we built Sentinel-UPI. It is a real-time fraud detection framework powered by Graph Attention Networks (GAT). Instead of looking at single transactions, Sentinel-UPI analyzes the entire transaction neighborhood. It assigns attention weights to high-risk nodes to find bad actors quickly. We tested our model using a modified PaySim dataset that mimics real Indian UPI traffic. The results were highly positive. Sentinel-UPI achieved an F1-score of 96.4% and an AUC-ROC of 0.98. It outperformed standard baselines like XGBoost and Random Forest by a wide margin. Our model, processes transactions in under 20 milliseconds. This speed perfectly meets the strict Service Level Agreements (SLAs) needed for live UPI networks.

A high-quality grain distribution system is the key to guarantee the balance of grain supply and demand, and the reduction of greenhouse gas emissions in grain transportation is the concern of the government and enterprises. In order to clarify the influence of different low-carbon policies and loading modes on the optimization of grain multimodal transport paths, this paper constructs a low-carbon grain multimodal transport path optimization model with the objective of minimizing transportation, cargo loss, time and carbon emission costs. Taking Jiamusi City, the main grain producing area in China, as an example, a heuristic genetic algorithm is used to solve the model to explore the impacts of carbon tax policy (CTP), carbon emission trading scheme (ETS) policy and carbon offset policy (COP) on the transportation schemes of grain in three loading modes, namely, “bagged, bulk and containerized”. We analyze the effects of carbon price fluctuations on decision-making under low carbon quota, medium carbon quota and high carbon quota scenarios, and study the effects of different cost preference values on transportation decision-making under the ETS policy. Under the ETS policy, the optimal transportation path of each loading mode has the lowest total cost, and the total cost is reduced by 1% compared with that of carbon tax and carbon offset policy. Among them, the containerized rail-water intermodal transportation scheme has obvious cost and environmental advantages, with the total cost decreasing by 42% and 33% compared to bag and bulk, and the carbon emission decreasing by 27% compared to both. With the overall relaxation of the time window, the transportation scheme is transformed from road-rail intermodal transportation to rail-water intermodal transportation. In addition, when the carbon price is RMB 2/kgCO_2 and above, it can promote the transportation transition to low-carbon rail-water intermodal transportation, and the high carbon quota under the ETS policy can motivate enterprises to realize cost reduction and efficiency. The findings of the study can provide reference for grain transportation enterprises to formulate multimodal transportation solutions and provide theoretical support for the government to formulate low-carbon policies.