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Development-Environment-Trade Nexus in BRICS: A Panel VAR Estimation
Abstract
Aims: This research examines the intricate associations among economic development, environmental degradation, and trade openness within the BRICS countries, providing visions for sustainable development policies aligned with global climate goals. Study Design: The study adopts a comprehensive panel data approach, analyzing 53 annual observations per country, totaling 265 observations from 1970 to 2023, to explore the interdependencies of key variables across Brazil, Russia, India, China, and South Africa. Place and Duration of Study: The research emphases on the BRICS countries, with data spanning from 1970 to 2023, utilizing 53 years of annual observations per country, aggregated to 265 data points. Methodology: The study employs advanced econometric methods, including panel unit root tests to assess stationarity, cointegration analysis to evaluate long-run relationships, panel vector autoregression to model dynamic interactions, and Granger causality tests to identify directional influences among variables such as GDP per capita, CO2 emissions, trade openness, and energy consumption. Results: Findings reveal strong positive correlations among all variables with no evidence of long-run cointegration, and significant causal pathways: variations in per capita GDP Granger cause variations in CO2 emissions, CO2 emissions Granger cause variations in trade openness, and variations in trade openness Granger cause variations in GDP per capita, highlighting complex interplays in these emerging economies. Conclusion: The results underscore the profound interdependencies between economic development, environmental impact, and trade dynamics in BRICS countries, offering critical insights for policymakers to design sustainable strategies that equilibrize economic development with environmental sustainability and global climate objectives.
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We examined the relationship of carbon dioxide (CO2) emissions with real per capita GDP and energy consumption in the ASEAN region using panel data from 1960 to 2021. We employed various panel unit root tests (IPS, Fisher-ADF, Fisher-PP) and panel cointegration tests (Kao and Pedroni). We find robust empirical evidence supporting the validity of the Environmental Kuznets Curve hypothesis, suggesting the existence of a per capita income turning point (approximately USD 4,808.85) where CO2 emissions and real income begin to decouple. This suggests the presence of an environmentally sustainable economic growth path beyond this threshold. The study highlights the importance of a reduction of fossil fuel use and the adoption of coordinated strategic plans among ASEAN member states to reduce carbon dioxide emissions.
The environmental Kuznets curve (EKC) has been the dominant approach among economists to modeling aggregate pollution emissions and ambient concentrations over the last quarter century. Despite this, the EKC was criticized almost from the start and decomposition approaches have been more popular in other disciplines working on global climate change. More recently, convergence approaches to modeling emissions have become popular. This paper reviews the history of the EKC and alternative approaches. Applying an approach that synthesizes the EKC and convergence approaches, I show that convergence is important for explaining both pollution emissions and concentrations. On the other hand, economic growth has a strong positive effect on carbon dioxide, sulfur dioxide, and industrial greenhouse gas (GHG) emissions, but weaker effects on non-industrial GHG emissions and concentrations of particulates. Negative time effects are important for sulfur and industrial and non-industrial GHG emissions. Even for particulate concentrations, economic growth only reduces pollution at very high income levels. Future research should focus on developing and testing alternative theoretical models and investigating the non-growth drivers of pollution reduction.
The authors explore the relationship between economic growth and environmental quality by analyzing patterns of environmental transformation for countries at different income levels. They look at how eight indicators of environmental quality evolve in response to economic growth and policies across a large number of countries and across time. Several conclusions are drawn; (1) income has the most consistently significant effect on all indicators of environmental quality; (2) many indicators tend to improve as countries approach middle-income levels; (3) technology seems to work in favor of improved environmental quality; (4) the econometric evidence suggests that trade, debt, and other macroeconomic policy variables seem to have little effect on the environment, although some policies can be linked to specific environmental problems; (5) the evidence shows that it is possible to"grow out of"some environmental problems, but there is nothing automatic about doing so - policies and investments to reduce degradation are necessary; and (6) action tends to be taken where there are generalized local costs and substantial private and social benefits.
This paper presents results concerning the size and power of first generation panel unit root and stationarity tests obtained from a large scale simulation study, with in total about 290 million test statistics computed. The tests developed in the following papers are included: Levin, Lin and Chu (2002), Harris and Tzavalis (1999), Breitung (2000), Im, Pesaran and Shin (1997 and 2003), Maddala and Wu (1999), Hadri (2000), and Hadri and Larsson (2005). Our simulation set-up is designed to address i.a. the following issues. First, we assess the performance as a function of the time and the cross-section dimension. Second, we analyze the impact of positive MA roots on the test performance. Third, we investigate the power of the panel unit root tests (and the size of the stationarity tests) for a variety of first order autoregressive coefficients. Fourth, we consider both of the two usual specifications of deterministic variables in the unit root literature.
In the first half of the paper I study spurious regressions in panel data. Asymptotic properties of the least-squares dummy variable (LSDV) estimator and other conventional statistics are examined. The asymptotics of LSDV estimator are different from those of the spurious regression in the pure time-series. This has an important consequence for residual-based cointegration tests in panel data, because the null distribution of residual-based cointegration tests depends on the asymptotics of LSDV estimator.In the second half of the paper I study residual-based tests for cointegration regression in panel data. I study Dickey–Fuller (DF) tests and an augmented Dickey–Fuller (ADF) test to test the null of no cointegration. Asymptotic distributions of the tests are derived and Monte Carlo experiments are conducted to evaluate finite sample properties of the proposed tests.
It is now quite common to have panels in which both T, the number of time series observations, and N, the number of groups, are quite large and of the same order of magnitude. The usual practice is either to estimate N separate regressions and calculate the coefficient means, which we call the Mean Group (MG) estimator, or to pool the data and assume that the slope coefficients and error variances are identical. In this paper, we propose an intermediate procedure, referred to as the Pooled Mean Group (PMG) estimator, which constrains the long run coefficients to be identical, but allows the short run coefficients and error variances to differ across groups. We consider both the case where the regressors are stationary and the case where they follow unit root processes, and for both cases derive the asymptotic distribution of the PMG estimators as T tends to infinity. We also provide two empirical applications: aggregate consumption functions for 24 OECD economies over the period 1962-93, and energy demand functions for 10 Asian developing economies over the period 1974-90.
Mitigation of carbon dioxide emissions has become an utmost important global agenda keeping into consideration the associated environmental hardships. As a result, it is important to unearth the factors which can neutralize carbon emissions to transform the world economy into a low-carbon one. Against this backdrop, this study explores the carbon dioxide neutralizing effects of economic growth, international tourism, clean energy promotion, and technological innovation for the cases of five European Union (EU-5) nations during 1990-2015. This study's main contribution is in terms of its approach to test the interaction effect between foreign direct investment (FDI) and energy innovation on carbon emissions. The econometric analysis chronologically involves the employment of unit root, cointegration, causality, and regression methods. The findings support the inverted-U-shaped economic growth-carbon emissions nexus to verify the Environmental Kuznets Curve (EKC) hypothesis. Besides, the Pollution Haven Hypothesis in the context of the selected panel is also verified as higher FDI inflows are seen to boost the emission levels. The results also confirm that energy innovation moderates the harmful effect of air transport (a proxy for international tourism) on carbon emissions during the developing stage of the tourism industry. On the other hand, renewable energy promotion is found to also curb carbon emissions. These findings suggest that the European governments need to expand investment in their renewable energy sectors and ensure the development of their clean industries, which can collectively help these nations become carbon-neutral in the future.
This textbook offers a comprehensive introduction to panel data econometrics, an area that has enjoyed considerable growth over the last two decades. Micro and Macro panels are becoming increasingly available, and methods for dealing with these types of data are in high demand among practitioners. Software programs have fostered this growth, including freely available programs in R and numerous user-written programs in both Stata and EViews.
Written by one of the world’s leading researchers and authors in the field, Econometric Analysis of Panel Data has established itself as the leading textbook for graduate and postgraduate courses on panel data. It provides up-to-date coverage of basic panel data techniques, illustrated with real economic applications and datasets, which are available at the book’s website on springer.com.
This new sixth edition has been fully revised and updated, and includes new material on dynamic panels, limited dependent variables and nonstationary panels, as well as spatial panel data. The author also provides empirical illustrations and examples using Stata and EViews.
“This is a definitive book written by one of the architects of modern, panel data econometrics. It provides both a practical introduction to the subject matter, as well as a thorough discussion of the underlying statistical principles without taxing the reader too greatly."
Professor Kajal Lahiri, State University of New York, Albany, USA.
"This book is the most comprehensive work available on panel data. It is written by one of the leading contributors to the field, and is notable for its encyclopaedic coverage and its clarity of exposition. It is useful to theorists and to people doing applied work using panel data. It is valuable as a text for a course in panel data, as a supplementary text for more general courses in econometrics, and as a reference."
Professor Peter Schmidt, Michigan State University, USA.
“Panel data econometrics is in its ascendancy, combining the power of cross section averaging with all the subtleties of temporal and spatial dependence. Badi Baltagi provides a remarkable roadmap of this fascinating interface of econometric method, enticing the novitiate with technical gentleness, the expert with comprehensive coverage and the practitioner with many empirical applications.”
Professor Peter C. B. Phillips, Cowles Foundation, Yale University, USA.
This paper explores the relationship between trade openness and CO2 emissions by incorporating economic growth as an additional and potential determinant of this relationship for three groups of 105 high, middle and low income countries. We apply the Pedroni (1999) and Westerlund (2007) panel cointegration tests and find that the three variables are cointegrated in the long run. Trade openness impedes environmental quality for the global, high income, middle and low income panels but the impact varies in these diverse groups of countries. The panel VECM causality results highlight a feedback effect between trade openness and carbon emissions at the global level and the middle income countries but trade openness Granger causes CO2 emissions for the high income and low income countries. Policy implications are also provided.
We examine the reduced-form relationship between per capita income and various environmental indicators. Our study covers
four types of indicators: urban air pollution, the state of the oxygen regime in river basins, fecal contamination of river
basins, and contamination of river basins by heavy metals. We find no evidence that environmental quality deteriorates steadily
with economic growth. Rather, for most indicators, economic growth brings an initial phase of deterioration followed by a
subsequent phase of improvement. The turning points for the different pollutants vary, but in most cases they come before
a country reaches a per capita income of $8000.
The current study investigates the causal relationship between economic growth and renewable energy consumption in the BRICS countries over the period 1971–2010 within a multivariate framework. The ARDL bounds testing approach to cointegration and vector error correction model (VECM) are used to examine the long-run and causal relationships between economic growth, renewable energy consumption, trade openness and carbon dioxide emissions. Empirical evidence shows that, based on the ARDL estimates, there exist long-run equilibrium relationships among the competing variables. Regarding the VECM results, bi-directional Granger causality exists between economic growth and renewable energy consumption, suggesting the feedback hypothesis, which can explain the role of renewable energy in stimulating economic growth in BRICS countries.
The object of this paper is to develop the methods and generalise the conclusions of Mr. Francis Gallon’s work on ‘Natural Inheritance.’ It endeavours to show the wide field which a purely statistical (as distinguished from a mechanical or physiological) theory of heredity may be made to cover. In order to do this it is needful to define certain biological terms in such a manner that they are capable of quantitative measurement, the symbols in terms of which they are expressed being the standard-deviations, correlation-coefficients, and regression-coefficients already well known from the labours of Mr. Galton.
The literature on trade openness, economic development, and the environment is largely inconclusive about the environmental consequences of trade. This study treats trade and income as endogenous and estimates the overall impact of trade openness on environmental quality using the instrumental variables technique. We find that whether or not trade has a beneficial effect on the environment varies depending on the pollutant and the country. Trade is found to benefit the environment in OECD countries. It has detrimental effects, however, on sulfur dioxide (SO2) and carbon dioxide (CO2) emissions in non-OECD countries, although it does lower biochemical oxygen demand (BOD) emissions in these countries. We also find the impact is large in the long term, after the dynamic adjustment process, although it is small in the short term.
We apply vector autoregression (VAR) to firm-level panel data from 36 countries to study the dynamic relationship between firms’ financial conditions and investment. By using orthogonalized impulse-response functions we are able to separate the ‘fundamental factors’ (such as marginal profitability of investment) from the ‘financial factors’ (such as availability of internal finance) that influence the level of investment. We find that the impact of financial factors on investment, which indicates the severity of financing constraints, is significantly larger in countries with less developed financial systems. Our finding emphasizes the role of financial development in improving capital allocation and growth.
This paper presents a critical history of the environmental Kuznets curve (EKC). The EKC proposes that indicators of environmental degradation first rise, and then fall with increasing income per capita. Recent evidence shows however, that developing countries are addressing environmental issues, sometimes adopting developed country standards with a short time lag and sometimes performing better than some wealthy countries, and that the EKC results have a very flimsy statistical foundation. A new generation of decomposition and efficient frontier models can help disentangle the true relations between development and the environment and may lead to the demise of the classic EKC.
A vast number of studies addressed the environmental degradation and economic development but not financial development. Moreover, as argued by Stern [2004. The rise and fall of the environmental Kuznets curve. World Development 32, 1419–1439] they present important econometric weaknesses. Using standard reduced-form modeling approach and controlling for country-specific unobserved heterogeneity, we investigate the linkage between not only economic development and environmental quality but also the financial development. Panel data over period 1992–2004 is used. We find that both economic and financial development are determinants of the environmental quality in BRIC economies. We show that higher degree of economic and financial development decreases the environmental degradation. Our analysis suggests that financial liberalization and openness are essential factors for the CO2 reduction. The adoption of policies directed to financial openness and liberalization to attract higher levels of R&D-related foreign direct investment might reduce the environmental degradation in countries under consideration. In addition, the robustness check trough the inclusion of US and Japan does not alter our main findings.
This is the new and totally revised edition of Ltkepohl's classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. It bridges the gap to the difficult technical literature on the topic as it is a highly accessible and user-friendly work. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.
A number of panel unit root tests that allow for cross-section dependence have been proposed in the literature that use orthogonalization type procedures to asymptotically eliminate the cross-dependence of the series before standard panel unit root tests are applied to the transformed series. In this paper we propose a simple alternative where the standard augmented Dickey-Fuller (ADF) regressions are augmented with the cross-section averages of lagged levels and first-differences of the individual series. New asymptotic results are obtained both for the individual cross-sectionally augmented ADF (CADF) statistics and for their simple averages. It is shown that the individual CADF statistics are asymptotically similar and do not depend on the factor loadings. The limit distribution of the average CADF statistic is shown to exist and its critical values are tabulated. Small sample properties of the proposed test are investigated by Monte Carlo experiments. The proposed test is applied to a panel of 17 OECD real exchange rate series as well as to log real earnings of households in the PSID data. Copyright © 2007 John Wiley & Sons, Ltd.
Asymptotic distributions and critical values are computed for several residual-based tests of the null of no cointegration in panels for the case of multiple regressors, including regressions with individual-specific fixed effects and time trends. The associated cointegrating vectors and the dynamics of the underlying error processes are permitted considerable heterogeneity across individual members of the panel. Copyright 1999 by Blackwell Publishing Ltd
This paper investigates how openness to international goods markets affects pollution concentrations. We develop a theoretical model to divide trade's impact on pollution into scale, technique, and composition effects and then examine this theory using data on sulfur dioxide concentrations. We find international trade creates relatively small changes in pollution concentrations when it alters the composition of national output. Estimates of the trade-induced technique and scale effects imply a net reduction in pollution from these sources. Combining our estimates of all three effects yields a somewhat surprising conclusion: freer trade appears to be good for the environment.
The relationship between cointegration and error correction models, first suggested by Granger, is here extended and used to develop estimation procedures, tests, and empirical examples. A vector of time series is said to be cointegrated with cointegrating vector a if each element is stationary only after differencing while linear combinations a8xt are themselves stationary. A representation theorem connects the moving average , autoregressive, and error correction representations for cointegrated systems. A simple but asymptotically efficient two-step estimator is proposed and applied. Tests for cointegration are suggested and examined by Monte Carlo simulation. A series of examples are presented. Copyright 1987 by The Econometric Society.
This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k,k being a finite non-negative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized t-bar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the t-bar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).
This paper examines whether electoral motives and government ideology influence short-term economic performance. I employ data on annual GDP growth in 21 OECD countries over the 1951-2006 period and provide a battery of empirical tests. In countries with two-party systems GDP growth is boosted before elections and, under leftwing governments, in the first two years of a legislative period. These findings indicate that political cycles are more prevalent in two-party systems because voters can clearly punish or reward political parties for governmental performance. My findings imply that we need more elaborate theories of how government ideology and electoral motives influence short-term economic performance.
This paper considers estimation and testing of vector autoregressio n coefficients in panel data, and applies the techniques to analyze the dynamic relationships between wages an d hours worked in two samples of American males. The model allows for nonstationary individual effects and is estimated by applying instrumental variables to the quasi-differenced autoregressive equations. The empirical results suggest the absence of lagged hours in the wage forecasting equation. The results also show that lagged hours is important in the hours equation. Copyright 1988 by The Econometric Society.
Using the result that under the null hypothesis of no misspecification an asymptotically efficient estimator must have zero asymptotic covariance with its difference from a consistent but asymptotically inefficient estimator, specification tests are devised for a number of model specifications in econometrics. Local power is calculated for small departures from the null hypothesis. An instrumental variable test as well as tests for a time series cross section model and the simultaneous equation model are presented. An empirical model provides evidence that unobserved individual factors are present which are not orthogonal to the included right-hand-side variable in a common econometric specification of an individual wage equation.
For the last ten years environmentalists and the trade policy community have engaged in a heated debate over the environmental consequences of liberalized trade. The debate was originally fueled by negotiations over the North American Free Trade Agreement and the Uruguay round of GATT negotiations, both of which occurred at a time when concerns over global warming, species extinction and industrial pollution were rising. Recently it has been intensified by the creation of the World Trade Organization (WTO) and proposals for future rounds of trade negotiations. The debate has often been unproductive. It has been hampered by the lack of a common language and also suffered from little recourse to economic theory and empirical evidence. The purpose of this essay is set out what we currently know about the environmental consequences of economic growth and international trade. We critically review both theory and empirical work to answer three basic questions. What do we know about the relationship between international trade, economic growth and the environment? How can this evidence help us evaluate ongoing policy debates? Where do we go from here?
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