Governance, urbanization, and pollution: A cross-country analysis of global south region

Figures & data

Table 1. Literature review summary

Author(s)	Period	Data	Country	Methodology	Results
Z. Wang et al. (2018)	1996–2015	Panel	BRICS Economies	PLS	Corruption has a significant effect on economic growth and carbon emission.
Lægreid and Povitkina (2018)	1972–2014		156 Countries	DCCE	An adverse effect of GDP per capita on CO2 emissions is not profound in the case of rich well-governed economies.
Candau and Dienesch (2017)	2007–2010	Count data	Three groups of countries based on governance	Negative binomial	Corruption lowers environmental quality.
Adu and Denkyirah (2017)	1970–2013	Panel	West African economies	FE, and RE	Economic growth increases emissions in the short-run but does not decrease in the long run.
Zhang et al. (2016)	1992–2012	Panel	19 APEC economies	Quantile Regression	The effect of corruption on CO2 emission is heterogeneous across countries and there exists an inverted U-shaped relationship between corruption and carbon emission.
Ozturk and Al-Mulali (2015)	1996–2012	Time Series	Cambodia	GMM, 2SLS	Control of corruption and better governance reduce pollution.
Sahli and Rejeb (2015)	1996–2013	Panel	MENA Region	GMM	Corruption affects pollution positively.
Goel et al. (2013)	2004–2007	Panel	100 Economies, MENA Region	2SLS	MENA regions have higher pollution rates; economies with higher corruption and shadow economy have lesser pollution rates.
Krishnan et al. (2013)	2004–08	Cross-section study	105 Economies	SEM	Proper management of e-govt. the system reduces environmental degradation via depressing corruption.
Biswas et al. (2012)	1999–2005	Panel	100 Countries	FE	The relationship between the shadow economy and the pollution level is dependent on corruption.
Ivanova (2010)	1999–2003	Panel	39 European Economies	MLE	Countries with effective regulation are likely to have relatively high emissions of sulfur dioxide.
Heckelman and Powell (2010)	1995–2005	Panel	82 Countries	OLS	The impact of corruption decreases once economic freedom increases.
Leitao (2010)	1981–2000	Panel	94 countries	IV	Increase in corruption impacts sulphur emission via increase in income.
Chang and Chang (2010)	1997–2004	Panel	62 Countries	Threshold VAR	Corruption has no adverse impacts on environmental pollution in countries with high corruption regime.
Koyuncu and Yilmaz (2009)	1980–2000	Panel	100 Countries	WLS	The relationship between corruption and deforestation are positive and significant.
Bernauer and Koubi (2009)	1971–2006	Panel	107 cities	FE, RE	Democracy, presidential forms of government are better quality compared to parliamentary form.
Pellegrini and Gerlagh (2006)	1997	Panel	EU Economies	OLS	Corruption is the key indicator in explaining the environmental indicator.
M. A. Cole et al. (2006)	1982–92	Panel	33 Countries	FE, OLS, and IV-2SLS	If corruption is high, FDI leads to lesser environmental stringency.
Bhattarai and Hammig (2001)	1972–91	Panel	66 Countries of Asia, Africa, and Latin America	OLS, F.E, and FGLS	Improvements in political institutions and macroeconomic policy curtail deforestation.

Notes: FE, RE, OLS, WLS, IV, FGLS, GMM, 2SLS, SEM, PLS, MLE, IV-2SLS, VAR, DCCE represents the fixed effects, random effects, weighted least squares, instrumental variables, feasible generalized least squares, generalized methods of moments, two-stage least squares, Structural Equation Modelling, Partial Least Squares, maximum likelihood, instrumental variables-two-stage least squares, vector autoregression, dynamic common correlated effects estimator, respectively.

Figure 1. CO₂ emissions and governance integrity (both in natural logs, 61 countries, 1990–2015)

Source: Own computations based on the datasets discussed in Section 2.2.

Figure 2. CO₂ emissions and transparency index (both in natural logs, 61 countries, 1990–2015)

Source: Own computations based on the datasets discussed in Section 2.2.

Table 2. Descriptive statistics and correlation of the key variables

	CO2	URB	IND	PEC	TI	GOI	HDI	FDI
Mean	1.419	1.668	1.347	1.042	1.280	1.075	0.556	0.483
Median	1.303	1.692	1.505	0.819	1.612	1.380	0.600	0.475
S.D.	0.815	0.181	0.509	1.144	0.688	0.644	0.186	0.365
Max	3.964	1.967	1.930	5.492	1.875	1.851	0.827	2.211
Min	0.000	1.215	0.000	0.000	0.000	0.000	0.000	−1.965
CO2	1.000
URB	0.328* (0.000)	1.000
IND	0.087* (0.005)	0.078* (0.001)	1.000
PEC	0.276* (0.000)	0.242* (0.000)	0.143* (0.000)	1.000
TI	0.022 (0.353)	−0.049* (0.049)	0.018 (0.464)	0.089* (0.004)	1.000
GOI	0.212* (0.000)	0.184* (0.000)	0.189* (0.000)	0.198* (0.000)	−0.116* (0.000)	1.000
HDI	0.359* (0.000)	0.383* (0.000)	0.131* (0.000)	0.307* (0.000)	0.067* (0.007)	0.397* (0.000)	1.000
FDI	0.061* (0.001)	0.015 (0.540)	0.127* (0.000)	−0.012 (0.616)	−0.049* (0.048)	0.210* (0.000)	0.073* (0.003)	1.000

Note: P values are reported in parentheses under the values of coefficients. (*), (**) and (***) denote the estimates significantly different from zero at 1%, 5% and 10% levels, respectively.

Table 3. Baseline findings, determinants of CO₂ emission (full-panel)

	(1)	(2)	(3)	(4)	(5)	(6)
	OLS	OLS	Quantile (50th)	Quantile (75th)	Quantile (50th)	Quantile (75th)
URB	−0.263* (0.090)	−0.353* (0.092)	0.368* (0.075)	0.205* (0.047)	0.299* (0.088)	0.226* (0.053)
PEC	1.575* (0.039)	1.871* (0.045)	2.007* (0.036)	2.071* (0.023)	1.576* (0.057)	2.021* (0.034)
GOI	−0.029* (0.009)		0.018 (0.022)	−0.030* (0.013)
TI	0.202* (0.012)		0.016 (0.018)	0.005 (0.011)
HDI	0.077* (0.030)	0.199* (0.030)	0.544* (0.080)	0.030 (0.049)	0.146*** (0.089)	−0.105* (0.053)
FDI	0.022* (0.010)	0.048* (0.011)	−0.130* (0.035)	−0.026 (0.022)	−0.156* (0.040)	−0.062* (0.024)
GOI×TI		−0.051* (0.005)			0.149* (0.015)	0.036* (0.009)
Constant	1.024* (0.129)	1.417* (0.133)	−0.002 (0.002)	0.758* (0.077)	0.229** (0.139)	0.734* (0.083)
Time Effect	Yes	Yes
Country Effect	Yes	Yes
R^2	0.981	0.979
Pseudo R^2			0.606	0.662	0.628	0.666

Notes: Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5% and 10% levels of significance respectively.

Table 4. Baseline findings, determinants of CO₂ emission (full-panel)

	(1)	(2)	(3)	(4)	(5)	(6)
	OLS	OLS	Quantile (50th)	Quantile (75th)	Quantile (50th)	Quantile (75th)
IND	0.042* (0.009)	0.041* (0.009)	−0.083* (0.025)	−0.074* (0.011)	−0.097* (0.031)	−0.075* (0.016)
PEC	1.570* (0.039)	1.857* (0.041)	1.993* (0.036)	2.095* (0.015)	1.607* (0.061)	2.046* (0.032)
GOI	−0.027* (0.009)		−0.012 (0.013)	−0.009 (0.009)
TI	0.201* (0.012)		0.017 (0.018)	0.008 (0.008)
HDI	0.070* (0.029)	0.192* (0.030)	0.924* (0.076)	0.036 (0.032)	0.364* (0.091)	−0.017 (0.019)
FDI	0.017** (0.010)	0.044* (0.011)	−0.068** (0.036)	−0.006 (0.015)	−0.079** (0.043)	−0.027 (0.022)
GOI×TI		−0.049* (0.005)			0.159* (0.016)	0.030* (0.008)
Constant	0.611* (0.035)	0.888* (0.032)	0.527* (0.056)	1.174* (0.024)	0.674* (0.062)	1.156* (0.032)
Time Effect	Yes	Yes
Country Effect	Yes	Yes
R^2	0.981	0.979
Pseudo R^2			0.605	0.665	0.624	0.668

Notes: Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5%, and 10% levels, respectively.

Table 5. Baseline findings, determinants of CO₂ emission (sub-panels)

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
URB	0.109 (0.108)	−1.533* (0.218)	0.991* (0.198)	0.151 (0.157)
IND					0.085* (0.018)	0.135* (0.041)	−0.077* (0.016)	0.011 (0.008)
PEC	1.232* (0.094)	1.840* (0.064)	1.459* (0.169)	0.618* (0.111)	1.180* (0.093)	1.804* (0.068)	1.171* (0.164)	0.631* (0.112)
GOI	−0.056* (0.024)	−0.008 (0.023)	−0.074* (0.014)	0.018 (0.012)	−0.051* (0.023)	−0.010 (0.015)	−0.072* (0.014)	0.019 (0.012)
TI	0.153* (0.022)	0.291* (0.025)	−0.103 (0.072)	0.047 (0.053)	0.158* (0.021)	0.257* (0.028)	−0.155* (0.073)	0.043 (0.053)
HDI	0.223* (0.080)	−0.123* (0.062)	−0.153* (0.067)	0.242* (0.033)	0.215* (0.078)	−0.153* (0.066)	−0.086 (0.062)	0.227* (0.031)
FDI	0.043** (0.025)	0.067** (0.038)	−0.006 (0.012)	−0.026* (0.011)	0.013 (0.015)	0.141* (0.039)	0.010 (0.011)	−0.025* (0.010)
Constant	0.565* (0.214)	3.204* (0.376)	−0.664** (0.348)	1.336* (0.361)	0.603* (0.063)	0.431* (0.072)	1.250* (0.138)	1.608* (0.0154)
Time Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Country Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
No of obs.	468	312	390	416	468	312	390	416
Region	Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America	Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America
R^2	0.975	0.983	0.980	0.994	0.976	0.981	0.980	0.994

Notes: All specifications are OLS estimations. All variables are converted into the natural log. Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5% and 10% levels of significance respectively. Columns 1, 2, 3, 4 represent the regression of CO₂ to the urbanization, while rest of the explanatory variables are remaining unchanged, similarly, Columns 5, 6, 7, and 8 represent the regression of CO₂ to the industrialization in place of urbanization, while rest of the explanatory variables are remaining unchanged.

Table 6. Baseline findings, determinants of CO₂ emission (sub-panels)

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
URB	−0.034 (0.035)	−1.375* (0.262)	0.952* (0.201)	0.170 (0.167)
IND					0.077* (0.019)	0.227* (0.047)	−0.071* (0.016)	0.012 (0.008)
PEC	1.291* (0.099)	1.958* (0.075)	1.517* (0.165)	0.604* (0.110)	1.250* (0.098)	1.924* (0.076)	1.277* (0.159)	0.617* (0.110)
GOI×TI	−0.114** (0.060)	−0.092 (0.073)	−0.154* (0.040)	0.006 (0.007)	−0.101** (0.059)	0.001 (0.007)	−0.152* (0.040)	0.066** (0.036)
HDI	0.228* (0.079)	0.084 (0.071)	−0.162* (0.066)	0.244* (0.032)	0.221* (0.078)	−0.001 (0.002)	−0.105** (0.062)	0.229* (0.031)
FDI	0.051** (0.026)	0.285* (0.042)	−0.003 (0.003)	−0.026* (0.010)	0.026 (0.027)	0.286* (0.041)	0.012 (0.013)	−0.024* (0.010)
Constant	1.016* (0.216)	3.008* (0.452)	−0.769* (0.336)	1.394* (0.352)	0.874* (0.056)	0.388* (0.082)	0.981* (0.056)	1.697* (0.112)
Time Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Country Effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
No of obs.	468	312	390	416	468	312	390	416
Region	Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America	Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America
R^2	0.972	0.975	0.980	0.994	0.973	0.975	0.979	0.994

Notes: All specifications are OLS estimations. Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5%, and 10% levels, respectively. Columns 1, 2, 3, 4 represent the regression of CO₂ to the urbanization, while rest of the explanatory variables are remaining unchanged, similarly, Columns 5, 6, 7, and 8 represent the regression of CO₂ to the industrialization in place of urbanization, while rest of the explanatory variables are remaining unchanged.

Table 7. Determinants of CO₂ emission

	(1)	(2)	(3)	(4)	(5)
CO2 (−1)	0.113* (0.030)	0.488* (0.150)	0.553 (0.467)	0.087 (0.086)	0.613*** (0.374)
URB	0.234* (0.058)	−0.405 (0.424)	−3.938 (3.030)	3.099** (1.872)	1.607 (1.378)
PEC	1.932* (0.048)	0.767* (0.274)	1.897* (0.244)	−0.592 (0.556)	0.472*** (0.293)
GOI	−0.007* (0.001)	0.014 (0.013)	−0.011 (0.010)	0.008 (0.007)	−0.059 (0.050)
TI	0.015* (0.001)	0.014* (0.005)	0.025* (0.009)	−0.010 (0.008)	0.010*** (0.006)
HDI	−0.149* (0.025)	−0.055 (0.051)	0.053 (0.051)	0.148 (0.133)	−0.233 (0.232)
FDI	−0.002 (0.002)	−0.012 (0.017)	0.021 (0.020)	0.007 (0.012)	0.003 (0.003)
Constant	0.388* (0.128)	1.108 (1.102)	6.573 (4.279)	−4.091*** (2.633)	−2.284 (2.270)
Region	Full-Panel	Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America
No of obs.	1525	450	300	375	400
No of instruments	55	55	55	55	55
Wald chi-square	3299.72*	104.77*	128.24*	130.50*	135.81*
Sargan P value	0.428	0.999	0.999	0.999	0.999
AR(1) P value	0.056	0.131	0.334	0.787	0.139
AR(2) P value	0.977	0.191	0.642	0.983	0.917

Notes: All specifications are System-GMM estimations. Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5%, and 10% levels, respectively.

Table 8. Determinants of CO₂ emission

	(1)	(2)	(3)	(4)	(5)
CO2 (−1)	0.129* (0.032)	0.377* (0.174)	−0.150 (0.102)	0.344 (0.297)	0.391 (0.271)
IND	0.029* (0.009)	0.005 (0.006)	0.214 (0.192)	0.003 (0.003)	−0.004 (0.005)
PEC	1.916* (0.042)	0.579* (0.287)	1.952* (0.308)	1.366* (0.637)	0.628 (0.463)
GOI	−0.008* (0.001)	0.012 (0.014)	0.045 (0.052)	−0.007 (0.007)	−0.020 (0.021)
TI	0.012* (0.001)	0.013* (0.005)	0.018 (0.014)	−0.003 (0.004)	0.008 (0.005)
HDI	−0.111* (0.017)	−0.061 (0.059)	−1.022 (0.725)	0.019 (0.017)	0.636 (0.462)
FDI	−0.002 (0.001)	0.009 (0.021)	0.111 (0.100)	0.002 (0.002)	0.003 (0.005)
Constant	0.711* (0.053)	0.694* (0.206)	1.268* (0.587)	0.549* (0.246)	0.287 (0.242)
Region	Full-Panel	Low Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America
No of obs.	1525	450	300	375	400
No of instruments	55	55	55	55	55
Wald chi-square	5135.69*	55.39*	307.70*	59.74*	546.39*
Sargan P value	0.417	0.999	0.999	0.999	0.999
AR(1) P value	0.036	0.238	0.753	0.131	0.227
AR(2) P value	0.991	0.474	0.719	0.446	0.360

Notes: All specifications are System-GMM estimations. Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5%, and 10% levels, respectively.

Table 9. Determinants of CO₂ emission

	(1)	(2)	(3)	(4)	(5)
CO2 (−1)	0.097* (0.029)	0.095 (0.094)	0.005 (0.005)	−0.027 (0.025)	0.716* (0.232)
URB	0.181* (0.052)	0.864 (0.851)	−3.622** (2.099)	4.788* (2.227)	0.488 (0.454)
PEC	1.992* (0.058)	1.696* (0.365)	2.262* (0.371)	−0.640 (0.702)	0.520** (0.270)
GOI × TI	−0.005* (0.0009)	−0.012 (0.013)	0.152 (0.123)	−0.017 (0.014)	−0.094 (0.082)
HDI	−0.163* (0.035)	−0.126 (0.091)	−0.149 (0.142)	−0.084 (0.067)	−0.005 (0.004)
FDI	0.004** (0.002)	−0.014 (0.014)	0.026 (0.025)	0.005 (0.004)	−0.001 (0.002)
Constant	0.496* (0.094)	−0.469 (0.415)	4.987* (1.263)	−6.106* (3.142)	−0.592 (0.541)
Region	Full-Panel	Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America
No of obs.	1525	450	300	375	400
No of instruments	54	54	54	54	54
Wald chi-square	3468.26*	208.26*	200.03*	181.00*	604.97*
Sargan P value	0.463	0.990	0.990	0.983	0.993
AR(1) P value	0.055	0.732	0.888	0.949	0.079
AR(2) P value	0.624	0.295	0.449	0.495	0.728

Notes: All specifications are System-GMM estimations. Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5% and 10% levels of significance respectively.

Table 10. Determinants of CO₂ emission

	(1)	(2)	(3)	(4)	(5)
CO2 (−1)	0.114* (0.030)	0.647* (0.176)	0.237 (0.216)	0.489** (0.280)	0.463** (0.274)
IND	0.027* (0.005)	−0.030* (0.015)	0.131 (0.090)	0.013 (0.012)	−0.003 (0.003)
PEC	1.980* (0.053)	1.198* (0.227)	1.494* (0.215)	1.116* (0.541)	0.585 (0.600)
GOI× TI	−0.005* (0.0009)	−0.012 (0.009)	0.041 (0.042)	−0.015 (0.014)	0.017* (0.008)
HDI	−0.155* (0.024)	−0.215* (0.060)	−0.289** (0.153)	−0.053 (0.059)	0.655 (0.535)
FDI	0.003** (0.001)	−0.023 (0.016)	−0.039 (0.041)	0.002 (0.003)	0.002 (0.003)
Constant	0.748* (0.041)	0.371* (0.180)	0.677* (0.324)	0.438** (0.236)	0.167 (0.162)
Region	Full Panel	Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America
No of obs.	1525	450	300	375	400
No of instruments	54	54	54	54	54
Wald chi-square	4697.41*	297.40*	615.78*	73.01*	1484.25*
Sargan P value	0.458	0.991	0.980	0.990	0.970
AR(1) P value	0.033	0.082	0.124	0.139	0.218
AR(2) P value	0.633	0.184	0.799	0.439	0.388

Notes: All specifications are System-GMM estimations. Standard errors are reported in parentheses. (*), (**) and (***) denote the estimates that are significantly different from zero at 1%, 5% and 10% levels of significance respectively.

Table A1. Definition, description, and sources of the variables

Variables	Unit	Description	Source	Expected Sign
Carbon emission (CO2)	Million metric tonnes	Burning of fossil fuels and manufacture of cement	EIA database, USA Government	Negative
Urbanization (URB)	Percentage of the total population	People living in urban areas	WDI, World Bank	Positive
Industrialization (IND)	Percentage of total economic growth	Increase in net industrial output in terms of overall industrial growth	WDI, World Bank	Positive
Primary Energy Consumption (PEC)	Quadra-billion btu	Total energy consumption of a country in a year	EIA, the USA government	Positive
Govt. Integrity (GOI)	Index	Government accountability in terms of overall administration and quality of governance	Heritage Foundation	Negative
Transparency Index (TI)	Index	Overall transparency in terms of government mechanism and functioning	Williams Index of transparency	Negative
Human Development Index (HDI)	Index	Overall human development indicating the key dimensions like a long and healthy life, being the knowledgeable and decent standard of living	United Nations Development Programme (UNDP)	Positive
Foreign Direct Investment (FDI)	Billions (in current US $)	Net inflows of foreign capital	WDI, World Bank	Positive

Notes: To avoid spurious regression in the model, all variables are converted into a natural logarithm. Govt. Integrity, transparency, HDI variables are in the form of an index.

Table A2. Classification of countries based on the criteria of World Bank Income Classification, 2017

Lower Middle Asia	Upper Middle Asia	Lower Middle Africa and Latin America	Upper Middle Africa and Latin America
Bangladesh, Bhutan, Cambodia, India, Indonesia, Jordan, Kyrgyzstan, Laos, Mongolia, Myanmar, Pakistan, Philippines, Sri Lanka, Syria, Tajikistan, Uzbekistan, Yemen, Vietnam	Azerbaijan, China, Iran, Iraq, Kazakhstan, Lebanon, Malaysia, Maldives, Russia, Thailand, Turkey, Turkmenistan	Bolivia, Angola, Cameroon, Congo, Cote D Ivoire, Egypt, Ghana, Kenya, Mauritania, Morocco, Nigeria, Sudan, Swaziland, Tunisia, Zambia	Brazil, Argentina, Colombia, Ecuador, Paraguay, Peru, Suriname, Venezuela, Algeria, Botswana, Equatorial Guinea, Gabon, Libya, Mauritius, Namibia, South Africa

Source: World Bank Income Classification Criteria, 2017 provided by World Bank

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