Abstract: Canada’s fossil fuel production is highly subsidized despite the pollution. In the Province of Alberta subsidies for oil and gas total approximately CAD$1.78 billion/year. This study quantifies the impacts of shifting fossil-fuel subsides towards solar photovoltaic (PV) capital investments. Although solar is already the lowest-cost form of electricity, such a subsidy shift would accelerate the renewable energy transition. This study found such a shift would enable installation of 1.53 GW of new solar PV capacity annually with the current investment tax credit (ITC) or 1.07 GW without it. These new solar PV systems can generate 2.02 TWh/year of clean electricity, if ITC is applied on capital investments and 1.41 TWh/year without it. The solar electricity is cost-competitive with natural gas generation, with levelized costs ranging from $49.01 to $61.97 CAD/MWh with ITC ($63.62 to $80.45 CAD/MWh w/o ITC) across Alberta. High-solar-resource locations in Alberta including Lethbridge ($49.01 CAD/MWh) and Calgary ($49.28 CAD/MWh) achieve lower costs than natural gas ($51.80 CAD/MWh). This excludes carbon externalities, fuel price volatility, and the long-term operational subsidies required to maintain fossil fuel competitiveness, suggesting that solar PV is already an economically rational alternative. Shifting Alberta’s fossil fuel subsidies is a solution for Canada's 2050 net-zero commitments. Solar-fossil fuel generation parity would be achieved by 2040 with ITC credits or 2045 without it. The subsidy redirection can reduce Alberta's grid emission intensity from the current 450 kg-CO₂e/MWh to 68.8 kg-CO₂e/MWh (with ITC) or 119.2 kg-CO₂e/MWh (without ITC) by 2050, representing reductions of 84.7% and 73.5%, respectively.

Abstract: The quality of governance is a key driver of resource mobilisation in a context marked by successive shocks that exacerbate fiscal imbalances. This study aims to analyse the role of institutional quality in the relationship between public expenditure and tax revenue in a panel of 162 countries, broken down into developed and emerging economies between 2000 and 2023. Using Dumitrescu and Hurlin's (2012) causality tests and the cross-sectional autoregressive model with staggered lags (CS-ARDL) to control for cross-sectional heterogeneity and cross-dependence, the results reveal a bidirectional causality linking expenditure and revenue for the entire panel; emerging countries are more sensitive to fiscal policies; public expenditure significantly stimulates tax revenue in the short and long term, with an effect amplified by institutional quality; long-term sustainability depends crucially on the institutional framework. This study highlights the need for targeted institutional reforms and fiscal rules differentiated according to countries' level of economic development.

Abstract: Saudi Arabia’s Vision 2030 places industrial upgrading at the center of economic diversification, yet the competitiveness impacts of energy inputs, renewable penetration, and innovation outputs remain insufficiently integrated in a single time-series framework. This study examines the determinants of Saudi industrial competitiveness, proxied by manufacturing value added (% of GDP), using annual data for 1990–2024 from the World Bank’s World Development Indicators. Methodologically, the analysis applies Augmented Dickey–Fuller (ADF) tests and an Autoregressive Distributed Lag (ARDL) bounds-testing approach with an error-correction model (ECM) to distinguish long-run equilibrium linkages from short-run adjustments. The ADF results indicate a mixed order of integration across variables, supporting the ARDL strategy. The bounds test provides evidence of cointegration at conventional significance levels. In the short run, technological innovation (total patent applications) is the only robust driver of competitiveness, while inflation is negative but only marginally significant and energy use, renewable energy consumption, GDP growth, and urbanization are statistically insignificant. The ECM term is negative and significant, implying rapid mean reversion, with about 55.5% of disequilibrium corrected annually. The findings suggest that innovation capability is the most immediate competitiveness lever, while energy-transition gains likely depend on longer-horizon efficiency, electrification readiness, and stable macro conditions.

Abstract: Climate change poses significant risks to New Zealand’s coastal agriculture through both slow-onset hazards (e.g., gradual sea-level–induced groundwater rise) and sudden-onset hazards (e.g., increasing frequency and severity of storms). These physical changes threaten the productivity and economic viability of coastal farms. However, few studies assess their combined economic impacts in a manner that supports land-use planning. This paper presents a conceptual framework to examine the implications of interacting slow- and sudden-onset climate hazards for New Zealand dairy farms, informed by real-world consultation with subject-matter experts to support feasibility analysis. We draw conclusions that illustrate the monetary impacts on farms associated with potential absorptive, adaptive, and transformational responses. The findings highlight the critical role of timing as environmental conditions deteriorate under climate change, as well as the need for policy frameworks that recognize and monetize the contribution of ecosystem services provided by coastal vegetation habitats to social, cultural, and environmental wellbeing. Incorporating these values into present-day financial decision-making is essential for supporting climate-related financial risk reduction and long-term land-use planning. Without such frameworks, the most beneficial land-use transitions are unlikely to be affordable or sustainable in New Zealand, especially towards year 2100.

Abstract: Although researchers document the political utility of cabinet reshuffles in African presidential systems extensively, they devote insufficient empirical attention to the trade-offs these reshuffles impose on economic performance. This paper delivers robust evidence that frequent ministerial changes inflict substantial, often underestimated costs on economic growth. The analysis draws on data from 19 African nations spanning 2006 to 2023 and applies a polynomial dynamic panel model, which uncovers a nonlinear relationship between cabinet stability and economic performance. Empirical estimates indicate that each cabinet reshuffle lowers annual GDP per capita growth by roughly 1.7 to 2.9 percentage points. The study further detects an inverted U-shaped relationship between ministerial tenure and economic outcomes. The Least Squares Dummy Variable Corrected (LSDVC) estimator reveals that ministers maximize economic growth at an optimal tenure of approximately 51.9 months (4.3 years). Beyond this threshold, moral hazard effects dominate the gains from accumulated experience. These results underscore a pivotal governance dilemma for African presidents: they must weigh the political benefits of cabinet reshuffles against their economic costs. The study thus advances insights into governance dynamics and economic performance in African settings.

Abstract: This paper explores the link between economic performance and multidimensional well-being in the Italian context using a combination of the ISTAT BES approach (Benessere Equo e Sostenibile) and machine learning and clustering analysis. On the basis of a dataset of 19 Italian regions and the Autonomous Provinces of Trento and Bolzano from 2012 to 2023, it will be examined how the three BES components—Benessere (B), Equità (E), and Sostenibilità (S)—are intertwined with the Gross Domestic Product of the regions. Regarding the Benessere (B) component of well-being, the Gross Domestic Product will be analyzed using a regression approach of the K-Nearest Neighbors type to reveal the complex linkages between health outcomes, education outcomes, working conditions, social participation, and economic performance. The clustering of the B indicators and the Gross Domestic Product will be done using Hierarchical Clustering analysis to identify homogeneous territories characterized by different levels of quality of life and economic prosperity. Regarding the Equità (E) component of well-being, the regression analysis will be done using the Boosting algorithm to model the linkages between the Gross Domestic Product and the indicators of income distribution, poverty, material deprivation, and inclusion in the labor market. Boosting regression analysis will be particularly useful for this purpose since it models the complex interactions and thresholds of social and economic inequalities. Hierarchical Clustering analysis will be applied to identify the territories characterized by different levels of equity and economic growth. Regarding the Sostenibilità (S) component of well-being, the Gross Domestic Product will be modeled using Boosting regression analysis to reveal the very complex linkages between the economic performance of the territories and the indicators of environmental quality, risk of climate change, innovation outcomes, and the quality of public services. For this purpose, the analysis will use the Random Forest algorithm to identify the territories characterized by different levels of sustainability and economic performance. The analysis will show that the BES approach provides a very useful framework to identify the very different levels of linkages between the economic performance of the territories and the outcomes of the BES approach. The analysis will provide evidence that the BES approach is a very useful framework for the analysis of the linkages between the economic performance of the territories and the outcomes of the BES approach.

Abstract: Over the past two decades, the world has experienced significant and relentless increase in environmental degradation, measured through carbon emissions (CO2). These emissions have been one of the persistent global concerns. South Africa boosts abundance of natural resources and some of the world’s most substantial mineral deposits endowment in the form of precious metals, diamonds and gold. The paper aims to examine impact of socio-economic and energy-related factors on environmental degradation from South African perspective. Using multivariate annual data spanning from 1991 to 2022, Autoregressive Distributed Lag Model (ADRL) was employed to determine both short-run and long-run impact of financial development (FD), renewable energy(RE), non-renewable energy (NRE), unemployment rate (UNE), economic growth (GDPPC), and population growth (PoPG) on CO2 emission. The results show that FD, RE, GDPPC, and PoPG promote environmental quality in the long run while NRE has opposite impact. The study thus calls for actions by relevant policymakers to stimulate economic growth and promote access to climate change finance, thereby encouraging investment in green energy technologies and consumption, to enhance and promote environmental quality in South Africa.

Abstract: The Glycaemic Index (GI) serves as a critical indicator of carbohydrate quality linked to postprandial glycaemic response. As “Low-GI” claims proliferate on front-of-pack la-bels, it remains unclear how consumers value this complex signal. This study quantifies willingness to pay (WTP) for Low-GI labeling and tests a “motivation–capability” mechanism, positing that health orientation motivates label use, while objective Low-GI knowledge facilitates targeted evaluation across nutritional contexts. A dis-crete choice experiment was conducted in China using plain yogurt (N = 910). Mixed logit models analyzed how the valuation of the Low-GI claim is moderated by carbo-hydrate context, health orientation, and objective knowledge. Results indicate a sig-nificant average premium for Low-GI labeling, with health orientation acting as a consistent motivational amplifier. Objective knowledge functions as a critical moder-ator interacting with carbohydrate context, driving label valuation only in specific low- or high-carbohydrate profiles while triggering skepticism in regular-carbohydrate ones. These findings suggest that the public-health effectiveness of emerging physiological claims depends jointly on consumer motivation and label-specific literacy. Conse-quently, policy interventions should combine label standardization with targeted ed-ucation, equipping consumers with the capability to decode the claim’s physiological meaning rather than relying on a generalized health halo.

Abstract: Electric-vehicle (EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid-systems framework that captures these complexities by integrating microfounded household choice, capacity constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang--bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. The framework provides a complex-systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies.

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Enset plays a vital role in providing both marketable and non-marketable goods and services for farming communities in South and Central Ethiopia. Recognizing the key attributes of enset production and leveraging scientific knowledge is essential for maximizing the resource’s potential and enhancing community welfare. This study aims to identify factors that affect marginal attributes of its production and estimate value of the economic goods and services of enset production. Using a cross-sectional survey with multistage sampling techniques, the study identified several significant economic benefits associated with enset production, including food, feed, fuel, medicine, fertility enhancement, soil moisture conservation, input of construction materials, fences and household items and soil and water conservation measures. The study found that the annual value of live enset and its processed three major products Kotcho, Bulla, and fiber amounted to 41.61 million and 3.79 billion Ethiopian birr, respectively. The feed, wrapping, income and cook benefits of the commodity’s estimated to be 171.9Billion Birr annually. Out of these additional attributes, feed value take the lion share (85%).The preferences of attributes varied across districts and households. The regression results revealed that marginal attributes of enset for food, feed, biodiversity, fertility, windbreak, wrapping and social cohesion is defined as a function of enset area, its source of harvest, family size, distance to market and farmer training centre characteristics of the commodity producers. The study identified key attributes of Enset production including food, feed, wrapping, biodiversity, social cohesion, land rehabilitation (fertility, moisture, windbreak, soil and water conservation) and fuel benefits that calls for immediate and long term research and development intervention. These findings underscore the critical importance of enset and emphasize the urgent need for strong policy and institutional support to ensure optimal utilization and sustainable development. The finding also illustrated that prompting Enset farming is inducing climate smart agriculture, provoking gender mainstreaming, assuring food and feed security.

Abstract: This paper examines the determinants of export performance in Asian landlocked developing countries and evaluates the impact of the United States-China trade war on their export outcomes. The study aims to extend the gravity model framework by incorporating trade-war phases alongside macroeconomic, logistical, and institutional factors to better understand the exposure of landlocked economies to external policy shocks. An augmented gravity model is applied to panel data covering twelve Asian landlocked developing countries and their major trading partners over the period from 2010 to 2024. The analysis employs Ordinary Least Squares and Poisson Pseudo Maximum Likelihood estimators with fixed effects to address heteroskedasticity, zero trade flows, and unobserved heterogeneity. The model includes economic size, geographic distance, logistics performance, regional integration, tariff measures, and interaction terms that capture different phases of the trade war. The results confirm the core predictions of the gravity model. Exporter and importer gross domestic product have positive and statistically significant effects on export flows, whereas geographical distance reduces trade intensity. Tariffs and trade-war-related shocks exert a significant negative impact on exports, with more potent effects during the second phase of the trade war. Improvements in logistics performance and participation in regional trade agreements enhance export performance. Although genuine exchange rate appreciation and higher importer income levels support exports, their positive effects are attenuated by increased trade policy uncertainty. The study relies on aggregate bilateral trade data, which may not capture firm-level adjustment dynamics. The findings highlight the importance of reducing trade costs, improving logistics infrastructure, and strengthening regional integration to enhance export resilience. The paper provides novel evidence on phase-specific trade-war effects on export performance in Asian landlocked developing countries, using an augmented gravity model.

Abstract: Accurate forecasting of central bank policy rates is critical for guiding monetary policy, shaping market expectations, and maintaining macroeconomic stability. In emerging economies such as Mongolia, conventional econometric approaches, including the Taylor Rule, ARIMA, and SVAR, often struggle to capture nonlinear dynamics, temporal dependencies, and structural breaks. This study addresses these limitations by developing and evaluating modern forecasting methods that combine machine learning and deep learning models within hybrid frameworks. The analysis employs a comprehensive monthly dataset of 26 macroeconomic indicators spanning January 2008 to December 2024. Seven models are constructed and assessed using RMSE, MAE, and R² metrics. The empirical results show that hybrid approaches, particularly XGBoost combined with Gradient Boosting and LSTM integrated with XGBoost, deliver the highest predictive accuracy, with the leading model reaching an R² of 0.9355. These hybrid methods consistently outperform both traditional econometric and standalone ML or DL models in capturing complex macroeconomic patterns and structural changes. The findings provide a robust data-driven framework to support evidence-based monetary policy in Mongolia and offer a transferable methodology for other emerging markets facing similar economic challenges.

Abstract: Digital transformation is a core strategic choice for enterprises to build sustained competitive advantages, and how to effectively help enterprises realize digital transformation is a hot and difficult issue discussed in academia and industry. The important impact of common ownership on micro-enterprise behavior may become an important grip for enterprise digital transformation. Using the data of Chinese A-share listed companies from 2007-2021 as a sample, we find that common ownership has a positive impact on corporate digital transformation, and the conclusion still holds after a series of endogeneity and robustness tests. The results of the mechanism test indicate that common ownership promotes corporate digital transformation by leveraging institutional synergies and supervisory governance effects. Further discussion reveals that the positive impact of common ownership on enterprise digital transformation is more significant in samples with faster economic growth and state-owned enterprises.This study provides new theoretical support and empirical evidence for the positive governance role of common ownership.

Abstract: The role of the U.S. dollar in central bank reserves is shrinking significatively world-wide. We identify three main reasons; first, U.S. and European financial sanctions, second, the U.S. inflation tax which reduces the real value of the USD, and third, the consequent dollar depreciation which increases the value of other reserve currencies and gold. We estimate several central banks’ diversification away from dollars, mostly towards gold, from 2015 to 2025. Our results suggest that these three factors explain the de-clining share of the dollar in reserves. As a consequence, the U.S. Treasury forgoes new seigniorage, the “exorbitant privilege” and inflation tax revenue.

Abstract: Digital technologies such as big data are reshaping resource allocation, raising interest in whether and how Heterogeneous science and technology innovation (STI) policies can help unlock urban carbon lock-in. Using panel data for 286 prefecture-level cities in China from 2009 to 2023, this paper examines the effects of heterogeneous STI policy intensity—classified as supply-side, demand-side, complementary-factor, and institutional-reform policies—on urban carbon unlocking efficiency. We develop a mechanism-based framework and empirically assess (i) the moderating roles of digital infrastructure, science and technology finance, and government green attention, and (ii) spatial spillover effects using spatial econometric models. The results show that all four policy types significantly improve local carbon unlocking efficiency, with institutional-reform policies exhibiting the largest marginal effect. When the four types are included jointly, only supply-side and demand-side policies retain statistically significant direct effects. Heterogeneity analyses indicate that demand-side, complementary-factor, and institutional-reform policies are more effective in low-pollution cities, whereas supply-side and demand-side policies have stronger effects in high energy-consuming cities. Mechanism tests further reveal that digital infrastructure amplifies policy effectiveness by facilitating factor mobility, science and technology finance strengthens policy impacts by easing financial constraints, and government green attention enhances policy effectiveness by improving implementation. Finally, carbon unlocking efficiency displays significant spatial dependence: supply-side and institutional-reform policies generate positive spillovers, while complementary-factor policies exhibit negative spillovers. Overall, the findings provide empirical evidence to inform the design and coordination of heterogeneous STI policy portfolios aimed at improving urban carbon unlocking efficiency.

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Purpose: This paper aims to systematically synthesize academic research published between 2020 and 2025 that investigates environmental, social, and governance (ESG) ratings and scores, with a focus on their methodologies, comparative performance, and impact on firm outcomes. Design/methodology/approach: A systematic literature review (SLR) was conducted using the Lens.org scholarly database. A structured title search retrieved 334 open access journal articles published between 2020 and May 2025 containing the terms "ESG Score", "ESG Rating", or "ESG Rater". The PRISMA 2020 protocol guided the selection and screening process. Findings: The literature exhibits growing concern about the divergence among ESG ratings, the methodological opacity of rating providers, and the variable financial implications of ESG scores. Common themes include score disagreements, rating agency biases, and emerging models for standardizing ESG assessments. Originality: This review provides the most up-to-date synthesis of ESG rating literature, focusing exclusively on articles explicitly addressing ESG ratings or scores in their titles. It contributes clarity to the fragmented ESG measurement space by organizing findings around key methodological and evaluative debates.

Abstract: The digital economy, while a pivotal engine for growth, presents a dual challenge in the context of climate goals. Utilizing panel data from 267 Chinese cities covering the period from 2011 to 2022, this study investigates the nonlinear relationship between the digital economy and carbon dioxide emissions, testing its conformity with the Environmental Kuznets Curve (EKC) hypothesis. Moving beyond a static verification, this research introduces a dynamic framework by examining how three exogenous shock variables—green finance, local government fiscal pressure, and climate policy uncertainty—reshape the EKC curve. Specifically, construct an extended EKC model with interaction terms to empirically assess how these exogenous shock variables shift the inflection point horizontally and vertically and alter the curve's slope. Our findings reveal that these factors significantly influence both the timing and peak level of emissions, as well as the efficiency of decarbonization before and after the turning point. This study provides a nuanced understanding of the digital economy's environmental impact, offering policymakers critical insights to navigate the green transition in the digital era.

Abstract: This research aims to analyze the influence of digital literacy, government policies, and infrastructure on coffee productivity through technology adoption in Kerinci Regency. A random sampling method was employed, and the sample size was determined using the Slovin formula, resulting in 95 respondents. Both primary and secondary data sources were utilized. Data were collected through observations, interviews, and questionnaires, and analyzed using a multiple linear regression model with SPSS 16.0 for Windows. The findings reveal that digital literacy, government policy, and infrastructure each have a significant impact on coffee productivity. Moreover, these three variables collectively exert a significant simultaneous effect on coffee productivity.

Abstract: This research evaluates how energy consumption per capita (ENUS) is affected by Environmental, Social, and Governance (ESG) factors, with particular focus on the Environmental pillar and its relationships with energy systems. The key research question is whether and how indicators related to ESG provide additional explanatory power for capturing variations in ENUS across countries and time, and whether machine learning techniques offer novel ways to address this problem beyond traditional panel econometric models. To this end, this research aims to combine panel econometric models and machine learning procedures, using a large dataset from the World Bank that provides internationally comparable data for approximately 161 countries during the period 2004-2023. From a methodological standpoint, this research will consist of three key steps. In a First Step, a sequence of fixed-effect, random-effect, and weighted least-squares models will be applied, with a specific focus on identifying how a large set of ESG-related variables relate to ENUS in a structural equation, controlling for country-specific unobserved heterogeneity. As a second step, this research will explore a sequence of clustering procedures, with a specific interest in identifying a number of regimes across which countries systematically co-pattern energy use with emissions, climate, and natural resources in a shared, multidimensional setting. In a third and final step, this research will evaluate a set of machine learning techniques through a sequence of assessments, with a specific focus on the K-Nearest Neighbor algorithm. It will identify that this technique is one of the most accurate models across a set of normalized criteria, such as accuracy and fit parameters. Model explanation will be improved through dropout loss and additive explanations, consistently assessing individual ESG variable weights in describing energy use. The analysis provides novel evidence that a strong and complex multideterminant pattern defines the relationships between ESG factors and energy use, with a strong influence from environmentally driven indicators, emissions intensity, energy efficiency, and natural resource usage, and with a complex interplay between social and governance pillar variables in driving energy consumption through development, institution, and structure variables.

Abstract: We propose a reproducible data-science workflow to diagnose partner–sector dependencies in the Kyrgyz Republic’s goods trade (2019–2024). HS-based flows are mapped into macro-sectors and transformed into partner indicators (turnover, net trade, import coverage, and role labels). Visual diagnostics and tables reveal a structural duality: (i) a China-centered import-deficit pole in manufactured goods and (ii) a narrow gold-driven export-surplus pole concentrated in the United Kingdom and Switzerland. We interpret the latter as surplus donors (donors of foreign-exchange inflows via trade) that partially offset the deficit pole. The pipeline is designed for repeatable monitoring of concentration risk and partner dependence.