Carbon Intensity Reduction Research Articles

ABSTRACTThis study explores the realm of environmental innovation as a driver of a low‐carbon economy, with a specific focus on the roles played by R&D activities and financial sector development. Centered in the context of Saudi Arabia, the research aims to explore how advancements in environmental technologies, coupled with robust R&D efforts and a thriving financial sector, can pave the way for a sustainable transition towards a low‐carbon economy. By examining the synergistic effects of environmental innovation, R&D investments, and financial sector development, the study seeks to elucidate the pathways through which these factors collectively contribute to fostering a greener and more sustainable economic landscape in Saudi Arabia. The empirical results confirm that environmental innovation has a significant impact on reducing carbon intensity. Moreover, interaction analyses reveal that the influence of environmental innovation on carbon intensity becomes stronger when supported by higher levels of financial development and R&D investment. The negative coefficients of the interaction terms signify a positive amplifying effect, indicating that finance and R&D enhance the ability of innovation to reduce carbon intensity. These findings underscore the importance of developing an integrated policy ecosystem that simultaneously fosters environmental innovation, enhances R&D investment, and promotes sustainable finance. For Saudi Arabia, such an approach is essential to achieving the Vision 2030 objectives of economic diversification and carbon neutrality. The study provides policymakers with practical insights, emphasizing the importance of targeted R&D programs, green financial instruments, and institutional coordination in promoting a sustainable and innovation‐driven low‐carbon transition.

Role of sustainable energy supply and green technology in accelerating the path towards carbon neutrality: Does fiscal development really matter?

This study aims to investigate the relationship between carbon pricing mechanisms and carbon intensity among India's top 100 publicly listed companies, as reported by the Carbon Disclosure Project (CDP). It specifically investigates how internal carbon pricing, science-based targets (SBTs), corporate social responsibility (CSR), and research and development (R&D) investment influence carbon disclosure practices and environmental transparency. A total of 253 firm-year observations from 2015 to 2021 were collected from CDP reports, OSIRIS financial database, annual reports, and India's National CSR Portal. The study applies multiple linear regression analysis to assess the influence of the identified variables on carbon intensity, measured as total CO₂ emissions (Scope 1, 2, and 3) per unit of sales. Content analysis was employed to validate disclosure attributes aligned with stakeholder and legitimacy theories. The regression results show a significant negative relationship between R&D and carbon intensity, highlighting the potential of innovation in reducing emissions. Science-based targets and CSR investments, however, show a significant positive association with carbon intensity, suggesting that high-emission firms are more likely to adopt visible sustainability initiatives. Internal carbon pricing was found to have no statistically significant influence on emission intensity. The findings provide actionable insights for Indian regulators and global policymakers. Emphasis should be placed on incentivizing science-based targets and R&D-driven decarbonization strategies while making internal carbon pricing mechanisms more effective. Investors can play a crucial role by demanding transparency, while firms must enhance their sustainability reporting frameworks to overcome barriers to disclosure and strengthen stakeholder trust. This study contributes to the accounting and environmental disclosure literature by being the first of its kind to empirically analyze the effect of internal carbon pricing and SBTs on carbon intensity in the Indian context. It offers timely, evidence-based insights relevant to achieving Sustainable Development Goal 13 (Climate Action) and supports global efforts in transitioning toward low-carbon economies.

Abstract China’s ecological civilization construction has reached a critical juncture, with carbon reduction emerging as its primary strategic imperative. As an essential platform for advancing ecological civilization, National Ecological Civilization Demonstration Zones (NECDZs) need deeper investigation regarding their role in facilitating a low-carbon transition. Utilizing panel data from 261 Chinese cities between 2007 and 2023, we apply a time-varying difference-in-difference approach to assess the impact of NECDZs on carbon intensity. Our research highlights a significant reduction in carbon intensity attributable to NECDZs, with notable disparities observed across varying marketization levels, resource endowments, industrial characteristics, and natural resource outgoing audits. Further analysis reveals that NECDZs indirectly reduce carbon intensity through collaborative governance involving government environmental regulation, enterprise green innovation, and enhancing public environmental awareness. These findings hold profound implications for the continuous advancement of ecological civilization and the low-carbon transition. Significance Statement This research addresses the urgent issue of global warming, which poses serious threats to our environment and future. By focusing on carbon emissions—a major contributor to climate change—and the world’s largest carbon emitter, China, we demonstrate how National Ecological Civilization Demonstration Zones (NECDZs) have effectively reduced carbon intensity. The findings show that this strategic initiative can significantly reduce carbon intensity, with notable variations depending on marketization levels, resource endowments, industrial characteristics, and natural resource outgoing audits. Our analysis also reveals that fostering collaboration among government environmental regulation, enterprise green innovation, and public environmental awareness is essential for driving impactful change, ultimately supporting a low-carbon transition.

ABSTRACT The role of local government debt (LGD) in emission reduction has recently become a hot topic. However, whether and how LGD influences carbon intensity remains unexplored. Using an endogenous growth model and China's province-level panel data from 2009 to 2019, this study represents an initial attempt to look into how LGD affects carbon intensity. Results demonstrate that LGD can increase carbon intensity and that LGD positively affects carbon intensity via suppressing technological progress. These empirical results align with our theoretical model's expectations. We also find that intergovernmental fiscal transfers rectify the positive effect of LGD, whereas fiscal decentralization reinforces it. Governmental departments should moderately control LGD by reducing the degree of fiscal decentralization and optimizing the system of intergovernmental fiscal transfers to reduce carbon intensity. Our study provides a valuable contribution to the LGD literature.

Towards net-zero electricity sector in emerging economies – Brazil, India, Indonesia and South Africa

Abstract The industrial sector, responsible for a substantial share of global greenhouse gas emissions, faces the dual challenge of advancing decarbonization while ensuring long-term competitiveness. Addressing this dilemma requires a transition toward renewable energy sources that not only reduce emissions but also enhance energy security and compliance with increasingly stringent climate regulations. This study examines the integration of renewable energy technologies into industrial processes, highlighting both opportunities and persistent barriers. On the benefits side, renewable adoption has the potential to deliver significant emission reductions, strengthen energy independence, and improve corporate reputation through alignment with sustainability targets. Nevertheless, limitations such as high initial investment costs, intermittency of supply, technological uncertainty, and unstable regulatory frameworks continue to hinder large-scale deployment. Emerging digital technologies, including machine learning for predictive maintenance and blockchain for energy traceability, are identified as enabling tools that improve efficiency, transparency, and integration across supply chains. By employing a narrative review methodology, this analysis synthesizes documented case studies and verifiable performance metrics to provide a structured view of current practices. Findings demonstrate that sector-specific renewable integration, such as solar thermal in manufacturing or green hydrogen in heavy industries is both technically feasible and economically viable under favorable conditions, yielding measurable reductions in carbon intensity. However, success depends on designing tailored strategies that consider local resource availability, fostering stable policy frameworks that reduce investment risk, and promoting cross-sector collaboration. Ultimately, a context-sensitive and adaptive approach emerges as essential to scaling industrial decarbonization without undermining competitiveness, ensuring that sustainability and productivity evolve in tandem.

Imported inputs reduce carbon intensity: Evidence from Indian manufacturing firms

Abstract Artificial Intelligence (AI) is emerging as a critical enabler of climate mitigation. However, its spatial and temporal impacts remain insufficiently understood. Using balanced panel data from 279 Chinese cities (2006-2019), this study examines how AI (measured by robot adoption) affects carbon emission intensity. We apply the Geographically and Temporally Weighted Regression (GTWR) model to uncover heterogeneous impacts. The results indicate that AI significantly reduces carbon emission intensity, thus contributing to climate mitigation. Nonetheless, the magnitude of this effect varies substantially across cities. The mitigation benefits are more pronounced in cities with limited natural resources, those located within the five major economic zones, and non-traditional industrial centers. These patterns reflect the presence of a “natural resource curse” in resource-rich cities and a “social resource blessing” in socially advantaged regions. The GTWR results further reveal pronounced spatial disparities, with eastern cities experiencing greater reductions in carbon intensity than those in the west. Over time, this spatial imbalance has been narrowing, indicating a gradual convergence in AI’s climate mitigation effects. These findings underscore the importance for regionally differentiated AI development strategies and policy interventions to reduce spatial inequities in mitigation capacity. The study provides robust empirical evidence from China, offering new insights into AI’s potential to support equitable and effective climate action on a global scale.experiencing greater reductions in carbon intensity than those in the west. Over time, this spatial imbalance has been narrowing, indicating a gradual convergence in AI’s climate mitigation effects. These findings underscore the importance for regionally differentiated AI development strategies and policy interventions to reduce spatial inequities in mitigation capacity. The study provides robust empirical evidence from China, offering new insights into AI’s potential to support equitable and effective climate action on a global scale.

Abstract Ambitious decarbonization mandates and rising volatility in energy markets demand decision-support tools that can learn, adapt, and generalize beyond the static optimization frameworks that dominate contemporary energy transition modelling. Addressing this gap, we articulate and validate a Deep Q-Network agent that learns investment policies in a custom OpenAI Gym environment tailored to the dynamics of national energy mixes. The environment is parameterized with an open Eurostat panel (2012–2021, 24 indicators, 430 country–year records), cleaned to 173 observations and compressed to a 16-dimensional state vector via PCA after k-NN imputation, outlier filtering, and min-max normalization. The agent selects among marginal allocations to renewables, fossil fuels, or nuclear capacity. Rewards balance renewable-share growth against carbon-intensity reduction, and episodes span 25 simulated years. After 1 200 training episodes (≈5 min on a Google TPU), the Deep Q-Network converges within 200 episodes and outperforms a myopic greedy baseline by 4.98 utility units (average reward = –19.34 ± 11.06 versus –24.32 ± 10.09), while delivering a 27% improvement in CO2-intensity metrics (0.421 versus 0.331 t CO2/MWh). From this paper, researchers can gain an extensible benchmark that integrates publicly reproducible data, an openly specified environment, and a modular MLOps pipeline. Practitioners—policy-makers, regulators, utilities, and clean-energy investors - obtain an interpretable agent whose learned policies expose the long-run trade-offs between renewable deployment speed and carbon-abatement efficacy. By demonstrating that deep reinforcement learning can produce stable, near-optimal strategies under structural uncertainty and high-dimensional state spaces, the study furnishes both methodological and practical impetus for embedding adaptive, data-driven intelligence in future energy-transition analyses.

Opportunities and Prospects for the Impact of the Green Deal on Reducing Carbon Intensity

The transition to a sustainable energy economy requires substantial public investment, with government spending playing a crucial role in driving the adoption of renewable energy and achieving environmental outcomes. This study investigates the impact of India’s budgetary allocations on renewable energy consumption, carbon emissions, and economic growth. The analysis covers annual data from 1990 to 2024. It employs the autoregressive distributed lag (ARDL) bounds testing approach and the Granger causality test to examine long-term equilibrium relationships and directional causality among the variables. The results indicate a statistically significant long-run relationship between government expenditure, renewable energy usage, and carbon emissions. Specifically, a 1% increase in renewable energy consumption (REC) results in a 1.14% decrease in carbon emissions, demonstrating the environmental benefits of clean energy deployment. The ARDL model also shows that past government disbursements significantly contribute to emissions reduction, with coefficients of –2147.41 (p < 0.001) and –997.36 (p < 0.05) at lags one and two, respectively. Granger causality results confirm an unidirectional causal relationship between renewable energy expenditures (REE) and carbon emissions, as well as between government spending and gross domestic product (GDP), highlighting the dual impact of such investment on environmental sustainability and economic growth. The findings underscore the effectiveness of public financial support in accelerating the transition to renewable energy while advancing macroeconomic goals. Strengthening and sustaining government investment in renewable energy is essential for achieving India’s long-term development targets, reducing carbon intensity, and promoting green economic growth.

This study investigates how various renewable energy technologies influence national carbon intensity (CO2 emissions per unit of GDP) across 184 countries over the period 2000–2020. In the context of Sustainable Development Goals (SDG 7 and SDG 13) and the post-Paris-Agreement policy landscape, it addresses the gap in understanding technology-specific decarbonization effects and the role of governance. A dynamic panel framework employing the Dynamic Common Correlated Effects (DCCE) estimator accounts for cross-sectional dependence and temporal persistence, while disaggregating total renewables into hydropower, wind, solar, and geothermal generation. Environmental regulation is incorporated as a moderating variable using the World Bank’s Regulatory Quality index. Empirical results demonstrate that higher renewable generation is associated with statistically significant reductions in carbon intensity, with hydropower showing the most consistent negative effect across all income groups. Solar and geothermal technologies yield substantial carbon-reducing impacts in lower-middle-income settings once supportive policies are in place. Wind exhibits heterogeneous outcomes: positive or insignificant effects in some high- and upper-middle-income panels prior to 2015, shifting toward neutral or negative after more stringent regulation. Interaction terms reveal that stronger regulatory environments amplify renewable-driven decarbonization, particularly for intermittent sources such as wind and solar. Key contributions include (1) a comprehensive global assessment of four disaggregated renewable technologies; (2) integration of regulatory quality into decarbonization pathways, illustrating post-2015 policy moderations; and (3) methodological advancement through a large-sample DCCE approach that captures unobserved common shocks and heterogeneous country dynamics. These findings inform targeted policy measures—such as prioritizing hydropower where feasible, strengthening regulatory frameworks, and tailoring technology strategies—to accelerate low-carbon energy transitions worldwide.

Emissions Reduction Visual Presentation R09 In recognition of the urgent need to reduce carbon footprint, regulations have been established to confront this critical challenge, acknowledging the significant and global impacts of carbon emissions. Globally, stringent policies, such as the European Union Renewable Energy Directive II and the Carbon Border Adjustment Mechanism, aim to internalise the social costs of carbon emissions and ensure global competitiveness while transitioning to a low-carbon economy. Additionally, the Safeguard Mechanism in Australia, administered by the Clean Energy Regulator, requires many large industrial facilities to reduce their emissions from their baseline levels. This regulatory framework drives emission reductions and encourages the adoption of cleaner technologies. Under this mechanism, standard facility baselines must decline by 4.9% each financial year through to June 2030. These regulations and incentive schemes create monetising opportunities for industries. Recognising this need, the management of carbon intensity gains prominence in today’s industry challenges. This paper explores how recent technological advancements can enhance the measurement, monitoring, reporting and reduction of carbon intensity across the hydrocarbon value chain. These improvements can substantiate projects while generating new revenue streams and differentiated products for energy suppliers. Additionally, the technology can then allow operators to control and optimise plant operations to meet carbon intensity targets for products. This capability differentiates products based on their carbon footprint, allowing companies to capitalise on market demand for levels of carbon intensity. Carbon intensity management will be an effective way to increase the bankability of green projects by providing a credible and auditable framework, digitally unlocking the benefits of reduced carbon intensity and monetising carbon reduction to make businesses more resilient to the changing environment. To access the Visual Presentation click on the link on the right. To read the full paper click here

Presented on 28 May 2025: Session 21 Liquefied natural gas’ (LNG’s) environmental credentials are under fierce scrutiny. Despite emitting half of the CO2 emitted by coal when burnt, critics point out that producing and shipping LNG to market is carbon intensive. Australia is home to some of the most emission-intensive projects in the world. Some LNG players have been proactive in looking at options to reduce the carbon footprint of their LNG projects, including detecting and reducing methane leakages, electrifying part of the liquefaction process and considering carbon capture and sequestration. However, progress has been slow. Buyers have so far shown little appetite to pay a premium for lower carbon emission LNG, limiting sellers’ resolution to commit to investments to reduce LNG’s carbon intensity. Could new regulation establish the foundations to create a premium market for low-carbon LNG? Much will depend upon the eventual level of taxation/penalties implemented and whether those will provide enough economic incentives to invest in reducing carbon emissions. The EU has reached an agreement to regulate methane emissions. Could LNG be added to its Carbon Border Adjustment Mechanism effectively resulting in an import tax at EU emissiosn trading system carbon prices? Would other LNG markets follow? In this paper, Wood Mackenzie will review the carbon intensity of LNG projects. We analyse the range of investments that could reduce carbon intensity to assess whether players have sufficient incentives to decarbonise different parts of the LNG value chain. Finally, we consider whether upcoming legislation will kickstart a premium market for low-carbon LNG and the implications this would have. To access the Oral Presentation click the link on the right. To read the full paper click here

ABSTRACTThis research investigates the joint environmental impacts of natural resource rents and FinTech in oil‐rich economies. It addresses the carbon curse hypothesis and argues that the integration of FinTech can be a transformative force, improving energy and carbon intensities in these countries. The study employs the Panel Spatial Correlation Consistent Least‐Squares Dummy Variables (PSCC‐LSDV) and Method of Moments Quantile Regression (MMQR) with a panel dataset of 20 oil‐rich countries from 1991 to 2022. The findings reveal that natural resource dependence has a significant impact on CO2 emissions and energy/carbon intensity, reinforcing the carbon curse resulting from excessive resource extraction. Furthermore, the study shows that different types of natural resource dependencies, excluding forest rents, lead to improved carbon/energy intensity in the energy mix, but only beyond a certain threshold of extreme resource extraction. The estimated turning point for this threshold is between 1.3824% and 9.2108% of GDP. This study provides the importance of proactive measures, including utilizing FinTech, to mitigate the environmental impact of excessive resource extraction, and highlights the significance of diversifying the energy mix and investing in renewable energy technologies to reduce carbon intensity and address the environmental consequences of natural resource reliance.

Green trade, governance, finance, and energy efficiency: Shaping environmental landscape in global powerhouses.

Does the promotion and application of new energy vehicles contribute to the reduction of carbon intensity? A quasi-natural experiment analysis

This study utilizes fuzzy-set qualitative comparative analysis (fsQCA) to explore the effects of critical economic and environmental factors on carbon intensity in 19 European countries from 1999 to 2020. The findings demonstrate a strong link between high carbon intensity, weak corruption control, and low research and development (R&D) expenditure, implying that corruption may intensify emissions by supporting carbon-heavy practices. Conversely, lower carbon intensity is associated with advanced financial development, suggesting a positive effect on reducing emissions, which contrasts with existing literature. Furthermore, effective corruption control and increased government revenue significantly mitigate carbon emissions by promoting investments in green initiatives. Other effective strategies identified include stringent environmental regulations, elevated environmental taxes, and enhanced R&D investment, all of which facilitate the adoption of carbon-neutral technologies. The results indicate that these structural relationships are statistically significant and affirm the hypothesis that robust corruption control, financial development, and R&D investment are vital for achieving reduced carbon intensity across Europe.

Is China on the right pathway to reduce carbon intensity? Based on green financial development with a novel GDI method