Ali Faridzad

Green finance is an important instrument for supporting low-carbon transitions; however, its effectiveness depends on targeted allocation that balances emission reduction with economic stability. In Malaysia, which has committed to achieving net-zero emissions by 2050, the sectoral distribution of green finance remains uncertain, particularly given complex inter-industry linkages. This study addresses this issue by developing a sectoral input–output framework to estimate indicative green finance thresholds associated with managing carbon transition risks. The approach integrates carbon intensity (CI) and Hypothetical Extraction Centrality (HEC) to capture environmental exposure and systemic importance across the national production network. The results suggest that implied green finance needs are concentrated in a limited number of sectors. Electrical and optical equipment emerges as a systemically important sector, while electricity, gas, and water supply show high carbon intensity. Additional sectors, including coke, refined petroleum and nuclear fuel, wholesale and retail trade, and basic and fabricated metals, are associated with relatively higher estimated financing levels. In contrast, service-oriented sectors appear to exhibit lower transition risks. Overall, the findings highlight how scenario-based, sector-specific allocation can provide insights into potential pathways for reducing emissions while maintaining macroeconomic stability. However, these estimates are best understood as indicative, model-based benchmarks that reflect the structural assumptions of a static, one-period input–output framework. The study offers a scalable analytical approach that may help inform green finance discussions in Malaysia and other emerging economies.

This study advances understanding of COz emission dynamics in Malaysia by integrating Structural Decomposition Analysis (SDA) with Machine Learning (ML) techniques to identify key emission drivers and evaluate their stability and predictability during 2010-2020. Using multi-year input-output tables and emissions data, an additive SDA framework decomposes changes in COz emissions into six effects: COz intensity, technology (production structure), household expenditure, investment, government expenditure, and exports. Four ML methods, namely Neural Networks with SHapley Additive exPlanations (SHAP), Lasso Regression, Elastic Net Regression, and Bayesian Regression, are applied to rank driver importance at aggregate and sectoral levels. Results show consistent findings across methods, with COz intensity emerging as the most influential and variable driver, followed by technology and household consumption. In contrast, government expenditure, investment, and exports contribute relatively little to emission changes. Sectoral analysis indicates that emissions fluctuations are primarily driven by electricity, gas and water supply, manufacturing, wholesale and retail trade, and transportation. SHAP results further confirm the significance of intensity improvements in the early decade, demand-driven growth in the middle years, and pandemic-related declines in 2019-2020. The findings provide evidence-based insights for climate policy, highlighting the importance of renewable energy adoption, technological upgrading, carbon taxation, and sustainable consumption in supporting Malaysia's net-zero ambitions and Sustainable Development Goal 13.

Defined by uneven responsibilities, burdens, and resource distribution in the pursuit of carbon neutrality, carbon emission inequality is simultaneously embedded in global structures of production, consumption, and trade that produce pronounced disparities across regions, sectors, and populations. Despite growing attention, existing studies often rely on national-level data or single accounting perspectives, limiting understanding of regional dynamics and long-term structural drivers. This study addresses these gaps by integrating both production-based and consumption-based emission measures within a regional analytical framework. Employing the Theil index and Index Decomposition Analysis (IDA) from 1995 to 2020, the research decomposed emission inequality into emission intensity, economic structure, and per capita value-added effects, enabling a comprehensive assessment of underlying drivers across five major regions: Asia, Europe, North America, Africa, and South America. Findings reveal a marked decline in global emission inequality, with production-based and consumption-based indices decreasing by 64% and 59%, respectively. Consumption-based inequality remained higher due to the carbon embedded in traded goods, highlighting the role of global supply chains in transferring environmental burdens from developed to developing regions. Emission intensity emerged as the most recurrent driver of inequality in Asia, Africa, North America, and globally, while structural factors dominated in Europe, and per capita value-added was most influential in South America. Regional convergence was observed, particularly driven by industrial growth and efficiency improvements in Asia, though disparities persisted in less industrialized regions.

Malaysia, as one of the most open economies in Asia, requires the strategic selection of key trade partners to ensure sustainable trade-led economic growth. This study employs two alternative approaches—the global extraction method (GEM), which is Malaysia-centric and measures Malaysia’s vulnerability to the removal of specific partners, and the Intercountry Trade Impact Index (ITII), which is system-wide and captures partners’ global significance through their interdependencies and economic weight—to analyse and rank Malaysia’s trade partners, comparing these results with actual trade performance data. The analysis utilises the 2020 OECD Inter-country Input-Output tables, aggregated to reflect Malaysia’s trade relationships. Results from the GEM highlight the Association of Southeast Asian Nations (ASEAN), South Korea, New Zealand, Australia and Japan as key trade partners. Alternatively, the ITII approach identifies major partners, including China, New Zealand, the United States, Other Europe and the rest of the world. An integrated ranking across all methods, including trade volume considerations, consistently positions ASEAN, Japan, China, South Korea and New Zealand as Malaysia’s top-five trade partners, offering a comprehensive and balanced strategy for re-evaluating Malaysia’s trade policies. JEL Codes: C67, F13, F14, F23, F63

The global shift toward a low-carbon economy has intensified scholarly and political attention on the governance of energy transition, which is critical for ensuring equitable, efficient, and sustainable outcomes. Despite the rapid expansion of research in this field, existing studies remain fragmented, with varying emphases on financial, policy, and technological dimensions, limiting the ability to form integrated governance frameworks. This systematic literature review addresses this gap by synthesizing current knowledge on “Energy Transition and Governance” through a rigorous search and selection process. Advanced database searches were conducted in Scopus and Web of Science, applying predefined inclusion and exclusion criteria to ensure relevance, quality, and novelty. From an initial pool of studies, 25 primary articles were identified for in-depth analysis. The findings were thematically organized into three core domains: (1) Financing, Economic Drivers, and Market Risks in Energy Transition, which explores investment flows, risk mitigation strategies, and the role of economic incentives; (2) Governance, Policy, and Justice in Energy Transition, which examines institutional frameworks, regulatory effectiveness, and equity considerations; and (3) Technological Innovation, ESG, and Data-Driven Solutions, which highlights the integration of emerging technologies, environmental, social, and governance (ESG) practices, and data analytics in accelerating transition goals. The review reveals that while financing mechanisms and technological advances are well-documented, policy coherence, cross-sectoral governance, and equitable justice frameworks remain underdeveloped. This underscores the need for future research to adopt interdisciplinary approaches, bridge policy–technology gaps and enhance governance models that align financial incentives with social equity and environmental integrity. By consolidating evidence across diverse perspectives, this study provides a comprehensive foundation for policymakers, industry leaders, and researchers seeking to design effective governance systems for a just and sustainable energy transition.

This study examines the role of financial services in Malaysia’s economy through a two-stage methodological framework. The first stage applies demand- and supply-driven input–output (IO) analysis to derive four indices: influence effect, induction effect, production-inducing effect, and supply-constraint effect. The second stage uses hybrid k-means hierarchical clustering to rank nonfinancial sectors based on their financial interdependence. Using Malaysia’s 2022 IO table and robustness checks from 2018 to 2021, results show that sectors like post and telecommunications; renting of machinery and equipment; wholesale and retail trade; and coke, refined petroleum, and nuclear fuel are highly influenced by financial services. Cluster analysis reveals that this key cluster is particularly sensitive to financial dynamics. Policy implications suggest that improving financial access, infrastructure, and resilience in this key cluster can accelerate Malaysia’s economic transformation. Aligning sectoral financing with IO linkages enables targeted interventions—such as credit guarantees and investment tools—to maximize multiplier effects and promote inclusive, sustainable growth.

This article employs two alternative approaches based on input–output tables to re-evaluate key Australian industries and their interconnections. The first approach utilizes traditional linkages, while the second approach employs machine learning methods. Data come from the OECD database for input–output tables spanning the years 2003, 2008, 2013, and 2018. Our findings indicate that employing machine learning methods including clustering with PCA, eigenvector centrality, authority and hub scores, and vertex centralities to identify key Australian industries not only confirming the ranking of key industries through traditional methods but also reveals the interconnectedness of key industries within clusters. While the former approach merely identifies key industries, the latter provides a more comprehensive analysis by demonstrating the ranking and connectivity of key industries, as well as validating the robustness of the applied methodologies.