Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China
TL;DR: In this article, the authors examined the impact factors of population, economic level, technology level, urbanization level, GDP per capita, industrialization level and service level on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model.
About: This article is published in Applied Energy.The article was published on 2013-06-01. It has received 421 citations till now. The article focuses on the topics: Population & Per capita.
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TL;DR: In this article, the authors adopted the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimated the relationship using different panel date models.
Abstract: Urbanization and industrialization have significant impacts on energy consumption and CO2 emissions, but their relationship varies at different stages of economic development. Taking cognizance of heterogeneity and the “ratchet effect,” this paper adopts the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimates the relationship using different panel date models. The main results are obtained by dynamic panel threshold regression models, which divide a balanced panel dataset of 73 countries over the period of 1971–2010 into four groups according to their annual income levels. The key results are: (1) in the low-income group, urbanization decreases energy consumption but increases CO2 emissions; (2) in the middle-/low-income and high-income groups, industrialization decreases energy consumption but increases CO2 emissions, while urbanization significantly increases both energy consumption and CO2 emissions; (3) for the middle-/high-income group, urbanization does not significantly affect energy consumption, but does hinder the growth of emissions; while industrialization was found to have an insignificant impact on energy consumption and CO2 emissions; (4) from the population perspective, it produces positive effects on energy consumption, and also increases emissions except for the high-income group. These novel methodology and findings reveal that different development strategies of urbanization and industrialization should be pursued depending on the levels of income in a bid to conserve energy and reduce emissions.
496 citations
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TL;DR: The results of cointegration tests suggest the existence of long-run cointegrating relationship among the variables, albeit with short dynamic adjustment mechanisms, indicating that the proportion of disequilibrium errors that can be adjusted in the next period will account for only a fraction of the changes.
427 citations
Cites background or result from "Examining the impact factors of ene..."
...Previous literature indicates that economic growth and energy consumption are major determinants of CO2 emissions (Wang et al., 2013)....
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...Similar results were found by Wang et al. (2013) in Guangdong, Paul and Bhattacharya (2004) in India, Elif et al. (2011) in Turkey, Acaravci and Ozturk (2010) in Europe, and Al-mulali et al. (2013) in Latin America and the Caribbean....
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TL;DR: In this article, the authors analyzed factors that influence carbon emissions due to fossil energy consumption in China to identify key factors for policies promoting carbon emission reductions and highlighted the policy implications in terms of industrial structure and energy consumption.
407 citations
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TL;DR: In this paper, the authors investigated the nexus among carbon dioxide emissions, economic and population growth, and renewable energy across regions using an unbalanced panel dataset of 128 countries over the period 1990-2014.
399 citations
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TL;DR: In this article, the authors examined the effects of environmental regulation and innovation on the carbon emission reduction of OECD countries during the period 1999-2014 and developed a new model called "stochastic impacts by regression on population, affluence, regulation and technology" (STIRPART) to extend the analysis on the evaluation of factors influencing carbon emissions.
396 citations
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TL;DR: In this paper, an estimation procedure based on adding small positive quantities to the diagonal of X′X was proposed, which is a method for showing in two dimensions the effects of nonorthogonality.
Abstract: In multiple regression it is shown that parameter estimates based on minimum residual sum of squares have a high probability of being unsatisfactory, if not incorrect, if the prediction vectors are not orthogonal. Proposed is an estimation procedure based on adding small positive quantities to the diagonal of X′X. Introduced is the ridge trace, a method for showing in two dimensions the effects of nonorthogonality. It is then shown how to augment X′X to obtain biased estimates with smaller mean square error.
8,091 citations
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TL;DR: In this paper, the authors argue that population growth causes a disproportionate negative impact on the environment and that the control of population is necessary but not sufficient means of seeing us through the whole crisis of environmental deterioration.
Abstract: There have been some questionable assertions relating to population growth. The most serious of these is the notion that the size and growth rate of the U.S. population are only minor contributors to this countrys adverse impact on local and global environment. The discussion in this article centers around 5 theorems which demonstrate the following: 1) population growth causes a disproportionate negative impact on the environment 2) the control of population is necessary but not sufficient means of seeing us through the whole crisis of environmental deterioration 3) population density is a poor measure of population pressure 4) environment as a term must be broadly construed to include physical environment of urban ghettos as well as the human behavioral environment and 5) theoratical solutions to out problems are not operational and some times are not solutions. The paper concludes that population control the redirection of technology the transition from open to closed resouce cycles the equitable distribution of opportunity and the ingredients of prosperity must all be accomplished if there is to be a future worth living.
2,381 citations
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TL;DR: In this article, the authors discuss a class of biased linear estimators employing generalized inverses and establish a unifying perspective on nonlinear estimation from nonorthogonal data.
Abstract: A principal objective of this paper is to discuss a class of biased linear estimators employing generalized inverses. A second objective is to establish a unifying perspective. The paper exhibits theoretical properties shared by generalized inverse estimators, ridge estimators, and corresponding nonlinear estimation procedures. From this perspective it becomes clear why all these methods work so well in practical estimation from nonorthogonal data.
1,828 citations
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TL;DR: In this paper, the STIRPAT model is augmented with measures of ecological elasticity, which allows for a more precise specification of the sensitivity of environmental impacts to the forces driving them.
1,628 citations
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06 Sep 2011TL;DR: An attempt is made to define multicollinearity in terms of departures from a hypothesized statistical condition, and measures are proposed here that fill this need.
Abstract: T O MOST economists, the single equation least-squares regression model, like an old friend, is tried and true. Its properties and limitations have been extensively studied and documented and are, for the most part, wellknown. Any good text in econometrics can lay out the assumptions on which common versions of the model are based and provide a reasonably coherent perhaps even a lucid discussion of problems that arise as particular assumptions are violated. A short bibliography of definitive papers on such classical problems as non-normality, heteroscedasticity, serial correlation, feedback, etc., completes the job. As with most old friends, however, the longer one knows least squares, the more one learns about it. An admiration for its robustness under departures from many assumptions is sure to grow. The admiration must be tempered, however, by an appreciation of the model's sensitivity to certain other conditions. The requirement that explanatory variables be truly independent of one another is one of these. Proper treatment of the model's classical problems ordinarily involves two separate stages: detection and correction. The DurbinWatson test for serial correlation, combined with Cochrane and Orcutt's suggested first differencing procedure, is an obvious example.' Bartlett's test for variance heterogeneity followed by a data transformation to restore homoscedasticity is another.2 No such "proper treatment" has been developed, however, for problems that arise as multicollinearity is encountered in regression analysis. Attention will focus here on what we consider to be the first step in a proper treatment of the multicollinearity problem its detection, or diagnosis. Economists are coming more and more to agree that the second step, correction, requires the generation of additional information.3 Just how this information is to be obtained depends largely on the tastes of an investigator and on the specifics of a particular problem. It may involve additional primary data collection, the use of extraneous parameter estimates from secondary data sources, or the application of subjective information through constrained regression, or through Bayesian estimation procedures. Whatever its source, however, selectivity and thereby efficiency in generating the added information requires a systematic procedure for detecting its need i.e., for detecting the existence, measuring the extent, and pinpointing the location and causes of multicollinearity within a set of independent variables. Measures are proposed here that, in our opinion, fill this need. The paper's basic organization can be outlined briefly as follows. In the next section the multicollinearity problem's basic, formal nature is developed and illustrated. A discussion of historical approaches to the problem follows. With this as background, an attempt is made to define multicollinearity in terms of departures from a hypothesized statistical condition, and * The authors are Associate Professor of Finance at the Sloan School of Management, M.I.T., and Assistant Professor of Business Administration at the Harvard Business School, respectively. We are indebted to Professor John R. Meyer for introducing us to the multicollinearity problem and for advice and encouragement during the present effort to place it in perspective, and to Professors John Lintner and Robert Schlaifer for their comments and criticisms. Responsibility for specific interpretations, especially erroneous ones, remains our own. This research was supported by the Institute of Naval Studies, of which both authors were members at the time the work was conducted, and by grants from the Ford Foundation to both the Sloan School of Management and the Harvard Business School. Computation time and facilities were provided by the Computation Centers of Harvard and M.I.T. 1J Durbin and G. S. Watson, "Testing for Serial Correlation in Least Squares Regression," Biometrika, 37-38, (1950-1951); and C. Cochrane and G. H. Orcutt, "Application of Least Squares Regression to Relationships Containing Autocorrelated Error Terms" Journal of the American Statistical Association, 44 (1949). 2F. David and J. Neyman, "Extension of the Markoff Theorem on Least Squares," Statistical Research Memoirs, II (London, 1938). 'J. Johnston, Econometric Methods (McGraw-Hill, 1963), 207; J. Meyer and R. Glauber, Investment Decisions, Economic Forecasting, and Public Policy (Division of Research, Graduate School of Business Administration, Harvard University, 1964), 181 ff.
1,506 citations