Modeling the Urbanization Rate Associated with Renewable in Electricity Production and CO 2 Emissions: A Threshold Regression Approach

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Modeling the Urbanization Rate Associated with Renewable in Electricity Production and CO 2 Emissions: A Threshold Regression Approach

https://doi.org/10.21203/rs.3.rs-4823821/v1

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This study examines the relationship between urbanization rates associated with the increasing dependency on renewable electricity production and CO₂ emissions per capita and gross domestic per capita in Jordan. The empirical analysis uses annual data from Jordan for the period 1990–2022 to answer the question: What is/are the threshold urbanization rate/s that pushes CO₂ emissions per capita to decline? Results suggest that two threshold values exist for Jordan, which implies a nonlinear relationship between urbanization rate and CO₂ emissions per capita. The estimated threshold regression model shows (0.78, 0.91) as the threshold values of urbanization rate, and values between these thresholds push CO₂ emissions per capita to decline. In addition, there is a significant negative relationship between these two variables.

JEL: R0; Q2; Q5; C24;

Urbanization rate

Renewable energy

CO2 emission

threshold regression

nonlinear relationship

This study aims to examine the relationship between the urbanization rate and the share of renewable in electricity production variables on CO2 emission in Jordan using the urbanization rate variable as threshold variable to determine the urbanization rate interval in which CO2 emissions begin to decline. Then examines the economic impact of urbanization rate and renewable in electricity production variables on GDP per capita.

By conducting the sequential F-statistics determined threshold test and the threshold regression, results reveal that the relationship between CO2 emissions per capita and urbanization rate is nonlinear. A high urbanization rate is associated with increasing dependency on renewable in electricity production which is targeting the national energy strategy in Jordan’s success to achieve economic development sustainability by decreasing CO2 emissions per capita and their positive effect on gross domestic per capita. The study recommends that Jordan should attract investments and encourage research in the clean energy sector since it reduces CO2 emissions.

Urbanization is attributed to the general growth in population, the extent of industrialization in a community, and the number and area of cities. This process includes a wide movement of individuals from rural to urban places characterized by great dimensions and densities. The process of urbanization is well organized in developed countries but is often very random in developing countries, where the bulk of firms are small and situated inside the city. Consequently, developing countries are considered to have the world’s highest levels of pollution, with such levels rising over time as a result of motorization, poor infrastructure, and poor urban planning, ultimately leading to traffic congestion (Grossman and Krueger 1995; Harari 2020).

Numerous theories attempt to explain urbanization's roots and consequences. The classical urbanization theory, one of the earliest theories of urbanization, postulates that economic forces like industrialization and the desire for greater employment prospects are what propel urbanization. The geographical layout of cities and the competition between various social groups for resources inside them are the subjects of another theory, referred to as the urban ecology theory. The deepest theory is the 21st-century urbanization thesis regarding the impact of globalization and technical improvements on urban growth and environmental sustainability. These theories continue to have an impact on environmental policies and urban planning, offering insightful information on the intricate relationship between emissions and urbanization. (Zhang,2016), (Mori and Aris, 2012), and (Lin et al, 2017)

According to the CO₂ Emissions Report 2022, carbon emissions increased by 0.9% in 2022, hitting 36.8 Gt. This increase was lower than expected due to the rapid utilization of clean energy technologies, which saved 550 Mt in carbon emissions. The sector with the greatest absolute growth in emissions was electricity and heat generation, which expanded by 1.8% in 2022, reaching an all-time high of 14.6 Gt. The report attributed this increase to the changing focus from gas to coal in emerging and developing Asian countries. With the rapid growth of urban populations around the world, the extensive construction of urban infrastructure and residential housing is unavoidable. Urbanization has become a primary factor affecting carbon emissions, rather than a secondary one, as a result of the accelerated energy consumption and carbon emissions caused by economic activities related to urbanization (Chen et al. 2015).

In Jordan, rapid population growth is bringing immense pressure due to the drastic increase in the demand for generating electricity. The energy sector contributes 73% of the greenhouse gases emitted in Jordan. In 2007, the Royal Commission for Energy reviewed and updated the national energy strategy to include the years 2007–2020, stipulating the need to reach 7% of the total from renewable energy by 2015 and 10% by 2020. To ensure the implementation of this strategy, the legislative authorities reinforced this trend by issuing a temporary law for renewable energy in 2010. Subsequently amended and approved as a permanent law in 2012, this law aims to prepare the appropriate groundwork for the entry of renewable energy into the energy mix. In 2013, for the first time, a new trend began with the private sector initiating projects to build large stations for renewable energy production without any costs borne by the government. By 2018, electricity generated from solar PV and wind had prevented the release of nearly 1.5 million tons of carbon emissions.

Creating a suitable model that can estimate the effect of urbanization on CO₂ emissions in Jordan is a very complex endeavor. Therefore, this study aims to examine the relationship between the urbanization rate and the share of renewable electricity production variables on CO2 emission in Jordan using the urbanization rate variable as the threshold variable to determine the urbanization rate interval in which CO2 emissions begin to decline. Then examines the economic impact of urbanization rate and renewable electricity production variables on GDP per capita. The importance of this study is answering the question which is can the urbanization rate associated with increasing dependency on renewable electricity production achieve economic development sustainability in Jordan by decreasing the CO2 emissions and positively affecting GDP per capita? The rest of the paper is organized as follows: section two provides the literature review, section three introduces the methodology and discussion, and section four presents the conclusions.

The issue of whether urbanization affects global CO₂ emission has been a source of substantial debate. According to Stern (2017), it is debatable from both a theoretical and empirical standpoint whether economic growth will initially worsen environmental problems before improving them. There is ample literature investigating the connection between urbanization and CO₂ emissions in the context of developing and developed countries. For example, Abdullah et al. (2023) show a significant positive connection between pollution and rapid urbanization. Meanwhile, using a time-resolved data series of CO₂ emission proxies to quantify urban CO₂ emissions in 91 cities, Luqman et al. (2023) reveal that urban CO₂ emissions are growing in all cities. A cluster analysis of factors shows that developing countries underwent rapid urbanization and rapid CO₂ emissions while developed countries underwent slow urbanization and slow CO₂ emissions. According to Bhat et al. (2022), the practical results from several empirical tools show a significant positive effect of urbanization on carbon emissions, which progressively worsen the environmental standard. Furthermore, Bassi et al. (2022) observe that firms situated on the busiest roads seek customer visibility, but by doing so, they put their workers at risk of actual pollution. By reviewing a panel of studies regarding the urbanization–climate–pollution series in West Africa, Ofoezie et al. (2022) document a persistent concern about pollution and show a potentially dark future of unsustainable environmental management. Khan and Su (2021) focus on the importance of balanced developmental perspectives for rural-urban regions and show the positive impact of urbanization on carbon emissions.

Sustainable economic growth is useful for enhancing environmental quality and decreasing environmental degradation, which is worsened by population growth (Adem et al. 2020). Kwakwa (2020) asserts that urbanization, trade, and financial development together apply upward pressure on carbon emissions. Mahmood et al. (2020) also show that industrialization and urbanization have a detrimental impact on the environment. Growing industrialization has a greater environmental impact than reducing industrialization. Using panel unit root tests, Yazdi and Dariani (2019) confirm that urbanization expands energy use and thus leads to more carbon emissions. Sun et al. (2018) reveal that both urbanization and economic growth may irritate pollution. Cetin et al. (2018) use the autoregressive distributed lag-bounds testing approach to cointegration and find that carbon emissions in Turkey are influenced by economic growth, energy consumption, and urbanization. Adom et al. (2018) and Alom et al. (2017) clarify that urbanization raises aggregate actual and potential carbon emissions. Moreover, Didenko et al. (2017) state that cities and all urbanized regions harm the global environment. York and McGee (2017) likewise argue that the interaction between income and energy may influence the eventual impact of income on carbon emissions.

Since China is in a stage of rapid urbanization growth, Chen et al. (2015) explain that this will pose a significant challenge to achieving China’s carbon emissions peak. Zhu et al. (2012) test the relationship between urbanization and carbon emissions in a sample of 20 emerging economies and shed new light on the urbanization–carbon emissions nexus. Bhuvandas (2012) reveals that as a result of rapid urbanization, the total number of motor vehicles in India doubled three times during the period 1990–2000, raising urgent concerns about air pollution. Martínez-Zarzoso and Maruotti (2011) examine the effect of urbanization on carbon emissions in developing countries and reveal an Inverted-U-shaped connection between urbanization and carbon emissions, with a positive relation for low urbanization levels. Additionally, York et al. (2003) confirm that urbanization and industrialization increase emissions.

The contribution of this paper to the empirical environmental economics literature is that this is the first study to use the threshold approach to determine how urbanization affects CO₂ emissions. This approach helps in understanding the short-term urban emission trajectories and requires quantifying trends in CO2 emissions from metropolitan areas. Determining the primary causes of these patterns will aid in revealing the longer-term drivers of emissions. This threshold effect regression enables us to judge the efficiency of adopting the policy of increasing electricity production from renewable in Jordan on decreasing CO₂ emissions per capita as the urbanization rate increases besides examining the economic impact on GDP per capita.

The study employs annual data over the period 1990–2022 from the World Bank. First, the study graphically plots the data trend to determine whether the form of the relationship between urbanization rate and CO₂ emissions per capita is linear. The vertical axis represents CO₂ emissions per capita (dependent variable), while the horizontal axis represents urbanization rates (independent variable).

Figure 1 clearly illustrates that the relationship is nonlinear and close to an inverted U-shape. The coordinates in the above figure are (0.73, 2.92), (0.78, 3.69), and (0.91, 1.9) for the beginning, peak, and trough points, respectively. By linking these coordinates, the graph shows an inverted U-shape. This study uses the threshold regression method to estimate the model for three reasons. First, threshold estimation enables us to avoid the collinearity/multicollinearity problem that appears with the quadratic equation (Narayan and Narayan 2010). Second, Fig. 1 shows that the dependent variable (CO₂ emissions per capita) does not tend to decline until the urbanization rate reaches the threshold value, which is approximately 0.78. Third, threshold regression is more consistent when different patterns of relationships exist (different regimes and policies). The study follows steps to estimate the relationship between the urbanization rate and share of renewables in electricity production (wind and solar) variables and the dependent variable (CO2 emissions per capita). The first step is conducting the sequential F-statistics-determined threshold test.

Table 1 shows the results of the sequential F-statistics determined threshold. The results show two threshold values for the urbanization rate (threshold repressor), which are (0.78, and 0.91). The threshold variable in this study (urbanization rate) is used to split the sample into three regimes or groups. The first and second groups respectively represent data points before and after the threshold value of the urbanization rate, which is 0.78. The third group represents data points after the trough value of the threshold variable, which is approximately 0.91. The sequential F-statistics test supports the tendency of this study to use the threshold approach for model estimation.

Table 1

Sequential F-statistics determined threshold
Threshold Test	F-statistics	Scaled F-statistics
0 vs. 1*	20.1540	60.4621
1 vs. 2*	6.4026	19.2080
2 vs. 3	1.5396	4.61
Threshold Values	Sequential	Repartition
1	0.783199	0.783199
2	0.911999	0.911999
Source: Using Eviews 12
*Significant at the 0.05 level

The next step to ensure that the threshold regression will reveal consistent results is conducting the unit root. Table 2 shows the result of the unit root test:

Table 2

Unit root test for the share of renewable in electricity production variable (at level)
		t-Statistics	Prob
Augmented Dickey-Fuller Test Statistics		-4.5878	0.0011
Test Critical Value:	10% level	-3.6891
	1% level	-2.9718
	5% level	-2.6251
Source: Using Eviews 12

According to the results in Table 2, the unit root test of the share of renewables in the electricity production variable (REP) shows that this variable is stationary at level. Therefore, regardless of the non-stationarity of the urbanization rate, there is no cointegration between the two time series.

Referring to the results in Table 1 the following equation is estimated before and after the threshold values for the regime

$$\:{CO}_{2}PC=\alpha\:+\:{\beta\:}_{1}{UR}_{t}+\:{\beta\:}_{2}REP+\:{\epsilon\:}_{t}\dots\:\dots\:..\left(1\right)$$

where

UR_t: urbanization rate/threshold repressor,

REP: share of renewables in electricity production (wind and solar),

B₁: coefficient of urbanization rate before and after the threshold value,

B₂: coefficient of the share of renewables in electricity production before and after the threshold value,

According to the threshold regression results in Table 4, the relationship between CO₂ emissions per capita and urbanization rate is nonlinear. This is because the sign of the coefficients of the urbanization rate turned from positive in Regime 1 (1990–2003) to negative in Regime 2 (2004–2018) and then to positive in Regime 3 (2019–2022).

Table 4

Results of threshold effects
Variable	Coefficient	Std. Error	t-Statistics	Prob
	$\:\varvec{R}\varvec{e}\varvec{g}\varvec{i}\varvec{m}\varvec{e}\:1:\varvec{U}\varvec{R}<0.7831\:\:\:\:\:\:\:\:\:\:14\:obs$
UR	2.840	4.95	0.573	0.5714
REP	-0.003	0.008	-0.469	0.6432
C	0.819	3.85	0.212	0.833
	$\:\varvec{R}\varvec{e}\varvec{g}\varvec{i}\varvec{m}\varvec{e}\:2:\:\varvec{O}.7831\le\:\varvec{U}\varvec{R}<0.9119\:\:15\:obs$
UR*	-7.217	0.98	-7.355	0.0000
REP*	-0.04	0.014	-2.808	0.0097
C*	9.334	0.832	11.207	0.0000
	$\:\varvec{R}\varvec{e}\varvec{g}\varvec{i}\varvec{m}\varvec{e}\:3:\:0.9119\:<UR\:\:\:\:\:\:\:\:4\:obs$
UR	266.69	159.57	1.671	0.1077
REP	-0.0979	0.075	-1.288	0.2098
C	-239.70	144.41	-1.659	0.1100
	$\:{R}^{2}=0.9038$ Adjusted R-Squared = 0.88717
	F-statistics = 28.1948 Prob (F-statistics) = 0.00	DW Stat = 2.36
Source: Using Eviews 12
*Significant at 1%

The threshold values of the urbanization rate are (0.78, and 0.91), which are consistent with the results of the sequential F-statistics determined threshold test shown in Table 1. Although the signs of the coefficient of the renewables in electricity production (REP) are negative in all regimes, these signs mean the REP controls the form of the relationship between urbanization rate and CO₂ emissions per capita. The mechanism of this control is that if the REP is below a specific percentage value, then the relationship is positive between the urbanization rate and CO₂ emission. By contrast, if the REP variable exceeds a specific percentage value, then the relationship will be negative. To support this view, the study carefully analyses the regression results in Regime 2 and compares these with the results in Regime 1.

In Table 4, the results of the threshold regression in regime 2 (2004–2018) show that the relationship between the independent variables urbanization rate and share of renewables in electricity production and the dependent variable CO₂ emission per capita is a statistically significant negative relationship. This means that Jordan successfully reduced its CO₂ emissions per capita despite the increasing urbanization rate. This success results from the country’s increasing dependency on renewable to generate electricity to cover the electricity demand caused by the increase in urbanization rate. A simple comparison between regimes 1 and 2 shows the compound growth rate of the share of renewables in electricity production for regime 2 (2004–2018), which grew by 24% while the compound growth rate of per capita electricity generation grew by 1.23%. While these compound growth rates in regime 1 (1990–2003) are very close, they reach 5% and 3.2% for the share of renewable in electricity production and per capita electricity generation, respectively. Depending on the results of threshold regression in regime 2, and as the urbanization rate in Jordan reaches approximately 0.91, the growth rate of renewable electricity production should be approximately 20 times bigger than the growth rate of electricity production from traditional resources to make an effect to decline CO₂ emissions. Therefore, Jordan should attract investments and encourage research in the clean energy sector.

The final step is to estimate the economic impact. To examine the effect of urbanization rate and share of renewables in electricity production on gross domestic product per capita the study estimates the following equation

$$\:GDPPC={\alpha\:}_{0}+\:{\alpha\:}_{1}{UR}_{t}+\:{\alpha\:}_{2}REP+\:{\epsilon\:}_{t}\dots\:\dots\:..\left(2\right)$$

GDPPC: GDP per capita

UR_t: urbanization rate

REP: share of renewables in electricity production (wind and solar),

$\:{\alpha\:}_{0}$ : intercept

$\:{\alpha\:}_{1}$ : Coefficient of urbanization rate after the threshold value

$\:{\alpha\:}_{2}$ : Coefficient of the share of renewables in electricity production before the threshold value

The OLS method and the period (2004–2022). The study choses this period due to this period includes the launching of the national energy strategy (2007–2020) and the current urbanization rate which is 0.91. According to the results in Table 5, the coefficients of urbanization rate and share of renewables in electricity production are positive and statistically significant at 10% level.

Table 5

Economic impact of urbanization rate and share of renewable in electricity production on GDP per capita
Variable	Coefficient	Std. Error	t-Statistics	Prob
UR	3493.208	1738.46	2.0093	0.0628*
REP	15.43	7.7707	1.9864	0.0656*
C	640.725	1497.44	0.4278	0.6748
	$\:{R}^{2}=0.5709$ Adjusted R-Squared = 0.5137
	F-statistics = 9.98 Prob (F-statistics) = 0.001752
Source: Using Eviews 12
*Significant at 10%

This means that as the urbanization rate and the share of renewable in electricity production increase, the GDP per capita increases too which is consistent with (Adem et al. 2020 and Sheng et al 2017).

In Jordan, rapid population growth and urbanization are bringing immense pressure due to the drastic increase in the demand for generating electricity. To ensure the implementation of the national energy strategy, the legislative authorities issued a temporary law for renewable energy in 2010. By 2018, electricity generated from solar PV and wind had prevented the release of nearly 1.5 million tons of carbon emissions. Therefore, this study aims to examine the relationship between the urbanization rate and the share of renewable in electricity production variables on CO2 emission in Jordan using the urbanization rate variable as threshold variable to determine the urbanization rates interval in which CO2 emissions begin to decline. Then examines the economic impact of urbanization rate and renewable in electricity production variables on GDP per capita.

By conducting the sequential F-statistics determined threshold test and the threshold regression, results reveal that the relationship between CO₂ emissions per capita and urbanization rate is nonlinear. A high urbanization rate is associated with increasing dependency on renewable in electricity production which is targeting the national energy strategy in Jordan’s success to achieve economic development sustainability by decreasing CO2 emissions per capita and their positive effect on gross domestic per capita. The study recommends that Jordan should attract investments and encourage research in the clean energy sector since it reduces CO2 emissions.

Authorship contribution statement

Ala’ Bashayreh : conceptualization, investigation, supervision, methodology, and writing- original draft.

Hatem Al-Hindawi (corresponding author): resources, methodology, formal analysis, writing–review and editing, and validation, software

Abla Tahtamouni: data presentation, writing–review and editing, and validation.

Declaration of competing interest

The authors affirm that they have no known financial or interpersonal conflicts that would have appeared to have an impact on the research presented in this study.

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No competing interests reported.

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You are reading this latest preprint version

Modeling the Urbanization Rate Associated with Renewable in Electricity Production and CO 2 Emissions: A Threshold Regression Approach

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Version 1

Abstract

Figures

Summary

1. Introduction

2. Literature Review

3. Methodology and Discussion

4. Conclusion

Declarations

References

Cetin, Murat, Ecevit, Eyyup, Yucel, Ali. (2018). Structural breaks, urbanization and CO₂ emissions: Evidence from Turkey, Journal of Applied Economics and Business Research, 8(2), 122–139.

Additional Declarations

Status:

Version 1

Variable	Coefficient	Std. Error	t-Statistics	Prob
	\(\:\varvec{R}\varvec{e}\varvec{g}\varvec{i}\varvec{m}\varvec{e}\:1:\varvec{U}\varvec{R}<0.7831\:\:\:\:\:\:\:\:\:\:14\:obs\)
UR	2.840	4.95	0.573	0.5714
REP	-0.003	0.008	-0.469	0.6432
C	0.819	3.85	0.212	0.833
	\(\:\varvec{R}\varvec{e}\varvec{g}\varvec{i}\varvec{m}\varvec{e}\:2:\:\varvec{O}.7831\le\:\varvec{U}\varvec{R}<0.9119\:\:15\:obs\)
UR*	-7.217	0.98	-7.355	0.0000
REP*	-0.04	0.014	-2.808	0.0097
C*	9.334	0.832	11.207	0.0000
	\(\:\varvec{R}\varvec{e}\varvec{g}\varvec{i}\varvec{m}\varvec{e}\:3:\:0.9119\:<UR\:\:\:\:\:\:\:\:4\:obs\)
UR	266.69	159.57	1.671	0.1077
REP	-0.0979	0.075	-1.288	0.2098
C	-239.70	144.41	-1.659	0.1100
	\(\:{R}^{2}=0.9038\) Adjusted R-Squared = 0.88717
	F-statistics = 28.1948 Prob (F-statistics) = 0.00	DW Stat = 2.36
Source: Using Eviews 12
*Significant at 1%

Modeling the Urbanization Rate Associated with Renewable in Electricity Production and CO 2 Emissions: A Threshold Regression Approach

Status:

Version 1

Abstract

Figures

Summary

1. Introduction

2. Literature Review

3. Methodology and Discussion

4. Conclusion

Declarations

References

Cetin, Murat, Ecevit, Eyyup, Yucel, Ali. (2018). Structural breaks, urbanization and CO2 emissions: Evidence from Turkey, Journal of Applied Economics and Business Research, 8(2), 122–139.

Additional Declarations

Status:

Version 1

Cetin, Murat, Ecevit, Eyyup, Yucel, Ali. (2018). Structural breaks, urbanization and CO₂ emissions: Evidence from Turkey, Journal of Applied Economics and Business Research, 8(2), 122–139.