Search Results (250)

This study aims to examine whether and how environmental, social, and governance (ESG) performance is related to corporate tax avoidance in a non-linear and threshold-dependent manner using explainable machine learning. Based on 6461 firm-year observations of publicly listed European firms over the 2018–2023 period, this study employs a multi-algorithmic machine-learning classification framework. Model interpretability is achieved through SHAP, which identifies feature importance, marginal effects, interaction patterns, and ESG-related threshold dynamics. The results demonstrate that the ESG–tax relationship is highly non-linear. While the Country and Industry factors establish baseline tax risks, ESG sub-dimensions act as critical firm-level determinants. Specifically, high Corporate Social Responsibility (CSR) and Human Rights scores effectively constrain tax avoidance. In contrast, exceptionally high Management scores correlate with increased tax-avoidance risk. These findings support the legitimacy buffer argument and show that strong governance may also reflect managerial sophistication and capacity for less visible tax planning. The study contributes by revealing non-linear ESG threshold effects and by demonstrating how XAI/SHAP can distinguish between symbolic and substantive sustainability practices in corporate tax behavior. Full article

In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China’s traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing’s computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China’s 14th Five-Year Plan. The “Eastern Data, Western Computing” strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of “construction–application–regulation” in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. Full article

This study investigates whether and how climate and environmental governance policy uncertainty shapes corporate tax avoidance. Using a comprehensive panel of 25,316 firm-year observations from 4700 Chinese listed firms over 2002–2024, we document that both climate and environmental governance policy uncertainty are associated with significantly lower effective tax rates, reflecting changes in firms’ tax planning behavior under policy uncertainty. Further, we show that this effect is economically and statistically transmitted through firms’ financing conditions. A battery of identification strategies, including lagged specifications, propensity score matching, and entropy balancing, confirms the robustness of the findings. Cross-sectional analyses further reveal that the effect is more pronounced among carbon-intensive, climate-sensitive, and less regulated firms. These findings imply that policy instability in climate and environmental governance may unintentionally incentivize corporate tax avoidance, thereby undermining both fiscal capacity and the effectiveness of environmental policy frameworks. Full article

This study investigates how the hydrogen supply chain should be designed under alternative carbon tax scenarios to decarbonize heavy-duty freight transportation. A bi-objective, multi-period optimization model is developed to minimize the total daily system cost while constraining CO₂ emissions using the Augmented ε-constraint approach, thereby revealing the trade-off between economic and environmental objectives. The model was applied to Türkiye’s heavy-duty transportation sector and solved under zero, moderate, and aggressive carbon tax scenarios. The results show that the levelized cost of hydrogen (LCOH) ranges from 2.06 to 14.06 $/kg H₂. High carbon pricing increases the LCOH by 29.06% in hybrid designs, while raising the renewable energy share from 2.04% to 46.97% in centralized supply chains. Sensitivity analysis reveals that a ±20% variation in electrolyzer-based production costs does not alter the network topology but shifts the LCOH between 13.10 and 15.02 $/kg H₂ in emission-focused solutions. The findings indicate that in renewable-energy-based decentralized structures, higher carbon tax policies primarily increase the LCOH. Still, the overall technology mix and network topology remain largely unchanged compared to the no-tax case. However, in centralized supply chains, carbon pricing affects both the energy sources and selected technologies. By integrating Türkiye’s 2030–2053 policy milestones into a multi-period framework, this study distinguishes itself by providing a comprehensive, multi-period planning framework tailored to the economic and logistical realities of developing countries. Unlike existing models, our approach quantifies how evolving carbon tax trajectories decisively drive infrastructure investment by analyzing the direct impact of different tax levels on the operational and strategic decisions of heavy-duty transport. This research represents the first joint assessment of carbon tax policy instruments and the evolution of long-term hydrogen supply chains, offering a decision-making framework for policy-driven energy transitions in similar emerging economies. Full article

Dhaka, one of the world’s most densely populated megacities, has faced a severe ecological decline, with green cover plummeting from 44.80% in 1975 to approximately 24.50% by 2005. In response, urban rooftop farming has emerged as a vital adaptation strategy to mitigate the urban heat island effect and air pollution. Objective: This study evaluates the transition from “pixels to policy” by testing automated identification methods for URF to support evidence-based urban governance, specifically the 10.00% holding tax rebate offered by the Dhaka North City Corporation. Utilizing high-resolution (3 cm) drone imagery across three diverse areas of interest—representing planned, organic, and mixed-use urban fabrics, the research compares the performance of Support Vector Machines, U-Net, and Text-Segment Anything Model. Accuracy was validated using a confusion matrix based on 1000 randomly stratified sample points. The SVM model emerged as the most reliable, achieving a Kappa index of 0.74 and 100.00% user accuracy for identifying rooftop vegetation, significantly outperforming the U-Net model (Kappa 0.14). Spatial analysis quantified a distinct “green divide,” revealing that while planned residential zones achieved over 7.50% rooftop greening coverage, dense organic settlements were limited to 6.00%. The study concludes that high-accuracy SVM-based identification provides a scalable foundation for automating fiscal incentives. To bridge the socio-spatial green divide, policy interventions must shift toward inclusive greening strategies, such as vertical farming, and formal integration of URF into Dhaka’s blue-green infrastructure networks. Full article

Electrification of road transport is widely promoted as a pathway to reduce greenhouse gas (GHG) emissions; however, its effectiveness depends critically on electricity carbon intensity, renewable energy share, charging behavior, and grid capacity constraints. This study develops a multi-objective analytical and optimization framework to evaluate cost and carbon-optimal electric vehicles electrification by jointly minimizing system cost and carbon emissions under coupled transport–energy system conditions. A closed form cut-off condition is derived to determine the minimum renewable electricity share required for electric vehicles to achieve lower emissions than internal combustion engine vehicles, and the formulation is extended to mixed fleets including battery electric and plug-in hybrid electric vehicles. The framework integrates fleet-level emissions, electricity demand, renewable capacity limits, charging losses, carbon taxation, and peak charging constraints to define a feasible electrification region. Feasibility mapping, Monte Carlo exploration, and evolutionary multi-objective optimization are employed to characterize trade-offs between CO₂ emission and total system cost, and to identify Pareto-optimal and knee point solutions. The results show that electrification without sufficient renewable support or coordinated charging can increase emissions and violate grid limits, whereas integrated planning enables significant emission reduction within economically viable regions. These findings provide a quantitative and decision-oriented basis for cut-off-informed and grid-aware electrification planning in carbon-constrained power systems. Full article

Open fiscal data in Ecuador remains largely unexplored beyond basic descriptive reporting, despite its potential for territorial intelligence and fiscal planning. This study examines how taxpayers are distributed across Ecuador’s provinces and economic sectors by applying a Big Data pipeline built on Apache Spark 3.5, PostgreSQL 14/PostGIS 3.2, and Python 3.11 spatial libraries to the SRI Tax Registry, comprising approximately 2.5 million records. The analysis combined K-Means and DBSCAN clustering with spatial autocorrelation methods, including Moran’s Index and LISA, to identify concentration patterns and territorial dependencies. The findings show that 68% of taxpayers are located in three provinces, namely Pichincha (34%), Guayas (24%), and Azuay (10%), with a spatial Gini coefficient of 0.61 reflecting considerable fiscal inequality across the country. A Global Moran’s Index of 0.49 (p < 0.001) confirms that neighboring provinces tend to share similar taxpayer densities, while LISA revealed five High–High clusters in major urban centers and six Low–Low clusters in the Amazon region and northern border. DBSCAN identified 27 spatial groupings, including secondary economic nuclei in cities like Ambato, Riobamba, and Machala that autocorrelation models alone do not capture. The methodology is replicable and offers a practical basis for designing place-based fiscal policies in similar contexts. These results provide tax authorities and regional planners with an empirically grounded, scalable framework for identifying territories with fiscal formalization gaps and designing geographically targeted interventions to reduce territorial inequality in Ecuador and in comparable developing-country contexts. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

This study examines how external regulatory conditions and internal organizational capabilities shape corporate tax compliance among foreign investment enterprises (FIEs) in Azerbaijan. It develops an integrated framework that brings together enforcement-based factors, tax planning capacity, and institutional and governance quality. Using survey data from 266 foreign-owned firms, the study applies structural equation modeling (SEM) to analyse direct, mediating, and moderating relationships. The results show that stronger enforcement is associated with higher levels of compliance and encourages firms to develop tax planning capabilities. In turn, these capabilities contribute positively to compliance behaviour. The findings also indicate that tax planning capacity partially mediates the relationship between enforcement and compliance. In addition, institutional and governance quality moderates the link between enforcement and tax planning capacity, with the effect varying across institutional environments. Overall, the results suggest that corporate tax compliance is influenced not only by external regulatory pressure but also by firms’ internal capabilities and the broader institutional context. The study provides useful insights for policymakers seeking to improve compliance through coordinated regulatory and institutional reforms. Full article

Corporate tax avoidance has become a major governance and fiscal sustainability concern, particularly in developing economies where corporate tax revenues constitute a critical source of public financing. While prior research suggests that board gender diversity (BGD) enhances ethical oversight and monitoring, its effectiveness in constraining aggressive tax planning may depend on firms’ informational and technological environments. This study examines whether artificial intelligence (AI) capability strengthens the governance role of BGD in reducing corporate tax avoidance. Using a balanced panel of 1586 non-financial firms from developing economies over the period 2009–2023, the analysis employs firm FE models and dynamic two-step System GMM estimations to address unobserved heterogeneity, endogeneity, and the persistence of corporate tax behavior. The results indicate that BGD is positively associated with effective tax rates, implying lower levels of corporate tax avoidance. Furthermore, AI capability—measured using a lagged specification—significantly strengthens this relationship, suggesting that firms with higher AI adoption exhibit a stronger governance effect of gender-diverse boards on tax compliance. Additional robustness tests—including alternative tax avoidance measures, alternative BGD specifications, heterogeneity analysis, and selection-bias corrections using Heckman, propensity score matching (PSM), and instrumental variable (2SLS) approaches—confirm the stability of the findings. Overall, the results highlight the complementary role of technological capability and board diversity in strengthening corporate governance (CG) and fiscal discipline in developing economies. Full article

Carbon capture and storage (CCS) is widely regarded as a viable pathway for reducing greenhouse gas emissions; however, large-scale deployment remains constrained by project economics and policy uncertainty. This study presents a life cycle costing assessment of a proposed CCS retrofit at the Hunter Unit 3 coal-fired power plant in Emery County, Utah, encompassing carbon capture, transport, and subsurface storage. Results indicate that the project appears economically favorable under the assumptions of this screening-level analysis and under current policy conditions, with an estimated break-even time of approximately five years. The analysis identifies a large upfront capital investment exceeding $600,000,000, offset by planned revenue from federal tax credits totaling several billion dollars over the project lifetime. Sensitivity analyses show that project economics are dominated by capture costs and annual mass of CO₂ sequestration rates, while storage and transport costs play secondary roles. A synthetic policy-perturbation analysis of the $85/ton tax credit further demonstrates that policy volatility materially increases uncertainty in investment returns. These results highlight both the economic potential of CCS retrofits at existing power plants and the critical role of stable long-term policy in enabling investment. Full article

The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. Through systematic literature review and analysis of Danish transport sector data, we develop the AI-Readiness Framework for Fleet Electrification (ARFFE), a modular decision support system adapted to different organizational readiness levels. Our secondary data analysis illustrates that two frequently overlooked factors, the CO₂-differentiated road tax savings of 430,000–465,000 DKK over five years and charging strategy decisions creating cost differences of 930,000 DKK, have greater economic impact than traditionally emphasized factors. The framework comprises five progressive modules mapped across four readiness stages and four planning dimensions, creating an integrated decision support system for evaluating an estimated 50,000+ scenarios. This research contributes theoretically by proposing AI as a “mediating technology” in socio-technical transitions and practically by providing an actionable framework illustrated through Danish transport sector analysis. Full article

Latin America faces growing challenges in the management of municipal solid waste (MSW). This is particularly evident in medium-sized and metropolitan cities where rapid urbanization, limited infrastructure, and high proportions of organic waste (40–70%) converge. This review synthesizes the most recent advances in organic waste management, valorization strategies, environmental performance, and policy frameworks in Mexico and Latin America. To provide a comprehensive overview, evidence from studies on informal recycling systems, route optimization, sustainable landfill siting, food waste valorization, life cycle assessments (LCAs), and biogas production is integrated. Techno-economic analyses of energy recovery from organic fractions are specifically reviewed. This review highlights that valorization of organic waste through composting, anaerobic digestion, food supplementation, and bioproduct generation can reduce greenhouse gas emissions by 40–70% compared to landfilling, with AD–composting hybrids achieving the highest reductions of 60–70%. Community composting achieved moderate reductions, 30–50%, but at significantly lower cost and with greater social co-benefits. These alternatives for valorizing the organic fraction extend the lifespan of both confined and open landfills. It also contributes to mitigating the public health impacts related to open dumping, disease vectors, and contaminated leachate. In short, this review also highlights shortcomings in policy coherence, financial mechanisms, source separation, and technology adoption. A strategic framework is proposed that prioritizes decentralized treatment systems, the integration of informal recyclers, tax incentives, community-based waste separation, and planning based on Life Cycle Assessment (LCA). The findings point to a viable strategy for transitioning from landfill dependency to circular waste management systems that improve the quality of life for the population of Latin America and the Caribbean. Full article

Digital tax reforms are implemented through staged, publicly announced milestones, yet policymakers rarely have timely indicators of whether these signals mobilize information-seeking and whether such demand can be anticipated for operational planning. We analyze monthly Google Trends series for Greece’s myDATA/e-invoicing rollout (2016–present) using preregistered event study models that separate step changes from post-event trend shifts with HAC-robust inference, and we evaluate 1–3-month predictive performance via rolling-origin cross-validation against a seasonal-naïve benchmark. Search-based attention shifts appeared most clearly in application-related queries: invoicing app terms spike around visible rollout phases (≈+34 to +38 index points over six months) and decline around VAT–myDATA alignment (≈−34 to −43). Ecosystem attention (the “Electronic invoicing” topic) exhibits large, opposite-signed movements (≈−53 around public-sector expansion; ≈+46 around VAT alignment), whereas platform terms show smaller and less regular responses; a back-office milestone produces no detectable change. In out-of-sample tests, event-aware regressions improve short-horizon accuracy for platform terms (≈40–50% MAE reduction at one month; ≈18–32% at two to three months), with series- and horizon-dependent results elsewhere. Overall, the evidence supports using search activity as an intermediate planning signal—informative about when and where guidance demand concentrates but not evidence of compliance. Full article

The liner shipping industry is thriving in the low-carbon transition, and optimizing operational strategies for liquefied natural gas (LNG) dual-fuel ships has become a research hotspot. This research examines the impacts of the carbon tax, emission control area (ECA) policies, fuel price discounts and methane slip rate on fuel management strategies. Firstly, to reduce liner operating costs and adhere to ECA policies, this study develops a basic optimization model. Further, the model is extended to take into account the impact of fuel price discounts. Secondly, by linearizing multiple nonlinear terms, the operational strategies are obtained. Thirdly, taking a real vessel sailing between the Far East and Northwest Europe as a case study, this study identifies the ports for LNG and very low sulfur fuel oil (VLSFO) bunkering, determines the bunkering amounts and calculates the planned speeds. Furthermore, sensitivity analyses are conducted on fuel price difference, carbon tax rate and methane slip rate. Results show that fuel price difference, carbon tax rate, methane slip rate and fuel price discount exert a significant impact on ship operational decisions. To ensure the effectiveness of maritime decarbonization regulations, authorities should monitor ship engines with high methane slip rates. This study offers important references for shipping enterprises to meet ship emission policies and simultaneously cut operational costs. Full article