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Energy Transition in BRICS Countries: The Role of Human Capital, Structural Transformation, and Institutional Quality: A Panel ARDL Approach
Get PDFThe BRICS economies (Brazil, Russia, India, China, and South Africa) represent a critical frontier in the global energy transition, balancing rapid economic development with pressing environmental imperatives. This study investigates the determinants of renewable energy adoption in BRICS countries from 2000 to 2022, employing a novel Panel ARDL methodology that addresses critical methodological gaps in existing literature. Using the Pooled Mean Group estimator and robust validation through Mean Group, Common Correlated Effects Mean Group, and Fixed Effects approaches, we analyze the synergistic effects of economic, structural, human capital, and institutional factors. Our findings reveal that gross fixed capital formation emerges as the most significant determinant, exhibiting a robust negative relationship with renewable energy share (coefficient: -0.172, p<0.01), indicating a pervasive technological lock-in effect from carbon-intensive investments. The error correction mechanism confirms a stable long-run equilibrium with moderate adjustment speed (18.3% annually), reflecting structural inertia characteristic of energy system transformations. Surprisingly, human capital and institutional quality demonstrate statistically insignificant impacts in aggregated analysis, though significant heterogeneity emerges at country level. The study contributes to the literature through its comprehensive methodological framework, innovative use of UNCTAD PCI indices, and nuanced policy insights. Our results underscore that investment reorientation—rather than investment volume—constitutes the primary lever for accelerating energy transitions in emerging economies, necessitating tailored approaches that account for BRICS members' distinct socioeconomic contexts and institutional frameworks
Institutional Quality as an Antidote to the Environmental Resource Curse: Evidence from CO₂ Emissions in MENA Economies
Get PDFThis study examines the complex relationships between institutional quality, natural capital, and CO₂ emissions in the Middle East and North Africa (MENA) region from 2000 to 2022. Using a comprehensive panel dataset across 10 MENA countries, we employ robust econometric techniques including fixed effects models in levels and first-differences, and system GMM estimators to address endogeneity and dynamic persistence. Our diagnostic framework incorporates tests for cross-sectional dependence, slope heterogeneity, and cointegration, revealing the absence of long-run equilibrium relationships in the region. The empirical results demonstrate that economic growth (0.48-0.60 elasticity), population pressure (0.73-0.95 elasticity), and investment patterns (0.05-0.09 elasticity) remain primary drivers of CO₂ emissions. Natural capital exhibits a significant positive relationship with emissions (0.11-0.29 elasticity), supporting the environmental resource curse hypothesis. Institutional quality shows a mitigating effect on emissions, though this relationship is complex and operates primarily through long-term channels. The absence of cointegration challenges conventional Environmental Kuznets Curve frameworks and underscores the region's environmental and economic instability. These findings highlight the urgent need for integrated policy approaches combining economic diversification, institutional reforms, and sustainable natural resource management to facilitate climate adaptation in this vulnerable region
Nobel Growth - An Understanding of 2025 Economics Nobel
Get PDFThis year’s Economics Nobel is provided for finding out the cause of innovation-destructive creation of new ideas. It is also highly individualistic. First, it neglects the welfare of those who lose the race and are destroyed. Can they assimilate this new knowledge and how? If not, then… Second, it neglects the very quality of creative destruction. In a capitalist society as Harrai (2014) argues innovation is always profit motivated. The discoverer of ORS, the simple thing that saved lives of million during dysentery is not recognized. Innovation of vaccine against malaria and dengue are still on a very primitive stage. Development of learning techniques that help first generation learners have taken a back seat to the hype in Artificial Intelligence.
The idea of creative destruction is appropriate to understand the evolution of the new world through a serious of continuous innovation and creation of new techniques, replacing the old ones. However, still there remain some broader aspects which the so-called growth theorists miss out. Yuval Noah Harari tries to point out some of the areas uncharted by the growth theorists. But, the ultimate vision of growth, as provided in the Mahayana doctrine is to lift all in a great vehicle
Nonparametric Identification and Estimation of Spatial Treatment Effect Boundaries: Evidence from 42 Million Pollution Observations
Get PDFThis paper develops a nonparametric framework for identifying and estimating spatial boundaries of treatment effects in settings with geographic spillovers. While atmospheric dispersion theory predicts exponential decay of pollution under idealized assumptions, these assumptions—steady winds, homogeneous atmospheres, flat terrain—are systematically violated in practice. I establish nonparametric identification of spatial boundaries under weak smoothness and monotonicity conditions, propose a kernel-based estimator with data-driven bandwidth selection, and derive asymptotic theory for inference. Using 42 million satellite observations of NO concentrations near coal plants (2019-2021), I find that nonparametric kernel regression reduces prediction errors by 1.0 percentage point on average compared to parametric exponential decay assumptions, with largest improvements at policy-relevant distances: 2.8 percentage points at 10 km (near-source impacts) and 3.7 percentage points at 100 km (long-range transport). Parametric methods systematically underestimate near-source concentrations while overestimating long-range decay. The COVID-19 pandemic provides a natural experiment validating the framework's temporal sensitivity: NO concentrations dropped 4.6\% in 2020, then recovered 5.7\% in 2021. These results demonstrate that flexible, data-driven spatial methods substantially outperform restrictive parametric assumptions in environmental policy applications
A Unified Framework for Spatial and Temporal Treatment Effect Boundaries: Theory and Identification
Get PDFThis paper develops a unified theoretical framework for detecting and estimating boundaries in treatment effects across both spatial and temporal dimensions. We formalize the concept of treatment effect boundaries as structural parameters characterizing regime transitions where causal effects cease to operate. Building on reaction-diffusion models of information propagation, we establish conditions under which spatial and temporal boundaries share common dynamics governed by diffusion parameters , yielding the testable prediction for standard detection thresholds. We derive formal identification results under staggered treatment adoption and develop a three-stage estimation procedure implementable with standard panel data. Monte Carlo simulations demonstrate excellent finite-sample performance, with boundary estimates achieving RMSE below 10\% in realistic configurations. We apply the framework to two empirical settings: EU broadband diffusion (2006-2021) and US wildfire economic impacts (2017-2022). The broadband application reveals a scope limitation --- our framework assumes depreciation dynamics and fails when effects exhibit increasing returns through network externalities. The wildfire application provides strong validation: estimated boundaries satisfy km and years, with the empirical ratio (72.5) exactly matching the theoretical prediction . The framework provides practical tools for detecting when localized treatments become systemic and identifying critical thresholds for policy intervention
Strategic Pricing and the Collapse of Protest Informativeness
Get PDFWe model a protest against a firm aiming to remove a product that causes negative externalities. Both the firm and consumers are uncertain about the product’s value, but consumers receive noisy signals. Price plays a key role in aggregating information. When prices are high, consumers with both good and bad signals derive almost the same utility from the product being sold, making protests uninformative. By endogenizing the price, we show that as consumer signals improve, protests become less informative, reducing social welfare. This suggests that consumer ignorance may play a role in protest success
Polynomial-Time Algorithms for Computing the Nucleolus: An Assessment
Get PDFRecently, Maggiorano et al. (2025) claimed that they have developed a strongly polynomial-time combinatorial algorithm for the nucleolus in convex games that is based on the reduced game approach and submodular function minimization method. Thereby, avoiding the ellipsoid method with its negative side effects in numerical computation completely. However, we shall argue that this is a fallacy based on an incorrect application of the Davis/Maschler reduced game property (RGP). Ignoring the fact that despite the pre-nucleolus, other solutions like the core, pre-kernel, and semi-reactive pre-bargaining set possess this property as well. This causes a severe selection issue, leading to the failure to compute the nucleolus of convex games using the reduced games approach. In order to assess this finding in its context, the ellipsoid method of Faigle et al. (2001) and the Fenchel-Moreau conjugation-based approach from convex analysis of Meinhardt (2013) to compute a pre-kernel element were resumed. In the latter case, it was exploited that for TU games with a single-valued pre-kernel, both solution concepts coincide. Implying that one has computed the pre-nucleolus if one has found the sole pre-kernel element of the game. Though it is a specialized and highly optimized algorithm for the pre-kernel, it assures runtime complexity of O(n^3) for computing the pre-nucleolus whenever the pre-kernel is a single point, which indicates a polynomial-time algorithm for this class of games
Do Demonstration Effects Catalyse Private Investment in Infrastructure? Evidence from the African Water Sector
Get PDFDemonstration effects play a perplexing role in the theories of change adopted by development banks and donors. Such effects are said to be important but cannot be measured or their mechanics fully explained. They are important because these organizations do not have enough funding to help developing countries fully cover the costs of achieving ambitious targets like the Sustainable Development Goals (SDGs), they must try to mobilize the balance of needed investment from the private sector. Some forms of direct mobilization are measurable, but demonstration effects are indirect and almost impossible to quantify. In theory, they occur when the success of a project supported by a development partner encourages replication by others using less concessional support. The importance of demonstration effects has grown as it has become clear that measurable types of mobilization account for far less private investment than is needed to achieve targets like the SDGs. Development partners repeatedly affirm this importance by routinely invoking these effects to justify subsidization of private investment projects. But proof of their existence—and importance—remains elusive. This paper uses a literature review and a case study of a notable public-private water sector investment project in Rwanda to evaluate the performance of demonstration effects in mobilizing private investment in infrastructure. Although the many development partners who supported this project confidently claimed that its demonstration effects were powerful and would prompt replication elsewhere, this investigation concludes that the project would most likely discourage replication by reasonably knowledgeable observers. Such conclusions suggest the need to better understand the nature of demonstration effects, why their existence is so widely taken for granted, as well as the wisdom of setting highly ambitious development targets like the SDGs which cannot be reached without significant levels of private investment
Private Sector Involvement in Higher Education in India: A State Level Analysis
Get PDFHigher education in India today is at the crossroads. There is a gradual shift from education being a government responsibility to its privatisation. The number of private unaided colleges and private universities has increased, share of enrolment in private institutions increases for most of the states in India. The study focuses on the status of private higher education enrolment of all states/regions in India and the factors influencing private higher education in India. There are considerable inter-state and inter-regional disparities in private higher education enrolment in India. As per AISHE report, in 2020-21, in India, 65 per cent of degree colleges are private unaided, only 21.4 percent colleges are fully public funded, 40.1 per cent of universities are private. Share of enrolment in private unaided college is 44.4 percent and in private aided college is 21.1 per cent; total share of private enrolment is 65.5 per cent. NSSO 71st round unit level data reveals that the private enrolment in higher education in India is about 58.4 percent. Privatisation in Southern and Western states is much higher than other states of India. Private enrolment in general courses is 42.2 per cent and in technical/professional courses it is 71.1 per cent. The picture is very clear that in professional and technical courses private enrolment is too high compared to general courses. Binary logistic regression results suggest that different socio-economic factors like religion, caste, gender, education level and occupation of household, type of courses are responsible for private enrolment of students in Higher education in India
Election and Subjective Well-Being: Evidence from the 2024 U.S. Presidential Election
Get PDFThis paper uses daily Behavioral Risk Factor Surveillance System data to estimate the causal effect of the 2024 U.S. presidential election, a highly competitive race whose outcome resolved lingering uncertainty on election day, on mental-health and life-satisfaction outcomes through a regression discontinuity design. Following the resolution of electoral uncertainty on election day, we find a sharp and persistent post-election decline in subjective well-being, concentrated among female, non-White, urban, and more-educated respondents. These findings reveal an expected-outcome shock, showing that political polarization itself, not electoral surprise, can act as a chronic psychological stressor