Carbon emission has increasingly become an issue of global concern because of climate change. Un- fortunately, Taiwan was listed as top 20 countries of carbon emission in 2014. In order to provide appropriate measures to control carbon emission, it appears that there is an urgent need to address how such factors as population and economicgrowthimpact the emission of carbon dioxide in any devel- oping countries. In addition to total population, both the percentages of population living in urban area (i.e., urbanization percentage), and non-dependent population may also serve as limiting factors. On the other hand, the total energy-driven gross domestic production (GDP) and the percentage of GDP generated by the manufacturing industries are assessed to see their respective degree of impact on carbon emission. Therefore, based on the past national data in the period 1990e2014 in Taiwan, an analytictool of Stochastic Impacts by Regression on Population, Afﬂuence and Technology (STIRPAT) was employed to see how well those aforementioned factors can describe their individual potential impact on global warming, which is measured by the total amount of carbon emission into the at- mosphere. Seven scenarios of STIRPAT model were proposed and tested statistically for the signiﬁcance of each proposed model. As a result, two models were suggested to predict the impact of carbon emission due to population and economicgrowth by the year 2025 in Taiwan.
It is clear that the impact of energy consumption on CO2 emissions is greater in the long-run followed by the short-run. The result is linked with the previous studies of Nilrit et al. (2017) , Ohlan (2015) and Hammami and Saidi (2015) where it confirmed that higher energy demand largely increases CO2 emissions in different economic settings. The impact of per capita GDP on CO2 emissions is negative, as if there is 1% increase in GDP, CO2 emissions decreases by -0.441% in the short-run and -0.634% in the long-run. Because increase in GDP enable a country to reach the same production level at lower CO2 emission by development of new low-carbon technology. The result confirmed that higher economicgrowth substantially decreases high mass carbonemissions from the country, hence it is imperative to sustained economicgrowth in the long-run. The result is linked with the previous studies of Zaman et al. (2015) , Hassan and Nosheen (2018) . where it confirmed that in Pakistan economy GDP support to reduce environmental degradation. The impact of populationgrowth on CO2 emissions is positive, which confirmed that high populationgrowth put a burden on environment in the form of high mass CO2 emissions, hence it is desirable to limit the population by family planning process in a country. The result is linked with the previous studies of Naqvi and Rehm (2014) , Bulut et al. (2017) and Yeh and Liao (2017) where it could find that high populationgrowth cumbersome the environment and largely deteriorate the environmental sustainability agenda across countries. The error correction term confirmed the model convergence, as its coefficient value is about -0.695, which reveals that the speed of adjustment towards the equilibrium is stable over a period of time. 4.6. CUSUM
The results reported in Table 7 and 8 shows the long-run and short- run analysis alongside the robust analysis of the long-run analysis impacts of carbon emission on population total, urbanization rate, gross domestic product per capita and energy use in Nigeria. Results in Table 7 show that in the long-run analysis population total and energy use have a significant and positive influence on carbon emission while in the short run analysis, all the independent variables have a positive impact on carbon emission but only energy use is significant. In addition, from the long-run result in Table 7, urbanization rate and gross domestic product per capita both have a negative impact on carbon emission. The results obtained for urbanization level negates the expected sign. This might be due to the
This study investigates the impact of economic and financial development on the level of carbon dioxide (CO2) emissions in sub-Saharan Africa (SSA) for the period 1989-2012 . The motivation for this investigation is a series of events that dates back to the awareness generated by the United Nations’ World Commission on Environment and Development (WCED). The report of the Commission has since 1987 declared the accumulation of CO2 as one of the environmental threats to the planet (GEO4 2007). The outcome of the increasing CO2 concentration in the atmosphere causes global warming, which leads to global climate change (Cunha-e-Sá 2008). The WCED’s declaration was followed by the establishment of the Intergovernmental Panel on Climate Change (IPCC) in 1988. It also led the journey to the United Nations Conference on Environment and Development which produced a number of international environment treaties including the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 (UNSD 2013). This event is marked as an acknowledgement to address human-driven climate change (Lattanzio 2014). During the exploration of the way forward to mitigating climate change, the United Nations Environment Programme (UNEP) introduced the Global Green New Deal (GGND) in 2009. The Deal is to encourage economic transformation to a green economy thereby reducing CO2 emissions and most likely its concentration in the atmosphere (GIZ 2013). Proactively, the African Development Bank (AfDB) introduced a ten-year strategy 2013-2022 to provide support for SSA countries on their climate change challenges and transition toward being a green economy (AfDB 2014a). Thus, this research examines how the selected explanatory variables affect CO2 emissions in SSA ex-post the year in which the issue of climate change started developing and ex-ante the AfDB ten-year strategy. The relevance of studying ex-
gression coefficient and the associated p-value are given for each decile of the conditional distribution. To stay within the scope of the paper, the coefficients carried by the control vari- ables are not displayed at this point of the study. However, the complete estimation outcome including the coefficients of the control variables and the related p-values are attached to this study. To illustrate the statistical variation of the co- efficients carried by the variables of interest along the entire conditional distribution of the dependent variable, a graphic display is provided. In each graph, the estimated quantile regression coefficients of the respective carbon emission cat- egory are plotted against the quantiles of the conditional dis- tribution. The solid horizontal line denotes the mean regres- sion estimate which does obviously not vary with the quan- tiles. The two dashed lines depict the conventional 95% con- fidence interval for the respective mean regression estimator. Looking at the graphical depiction presented above re- veals that carbonemissions from all sources affect firm value similarly along the conditional distribution. However, it should be noted that the coefficients obtained from the sec- ond model, referring to the impact of Scope 2 carbon emis- sions on firm value, are much higher at each specific decile than the estimators provided by the first and the third model. Nevertheless, at the bottom of the conditional distribution, the quantile estimators carried by the variable of interest are close to zero, but always negative, in all three models. The effect of direct carbonemissions on firm value at the lowest decile is not found to be statistically significant at the 10% level. From the conditional distribution’s bottom until approximately the median, the quantile-specific coefficients in all models are roughly constant as they show only minor changes with a tendency to decrease (increase in absolute value). In contrast, at the right side of the conditional distri- bution, the coefficients carried by the independent variables diminish strongly reaching the lowest (highest absolute) value at the highest quantiles. Except the mentioned in- significance, all quantile estimators in all three models are significant at the 10% level.
These rural areas are basically supported with rubber and oil palm agricultures without high demand for energy resources. Majority of the population living in urban settlement relatively enjoy high standards of living with quality infrastructure, electricity, telecommunication and clean water supply facilities. Although resource rich states such as Terengganu, Sabah, Sarawak and Pahang attract resource based industries, but most of the export based industries are located in the Malay Peninsula. Besides, this urban area is advantaged with accessibility, infrastructure, transportation and high skilled manpower. The recent study of Shahbaz et al. (2015) found strong causal links between urbanization and energy consumption in Malaysia. Therefore, there is a possibility that the urbanization also have direct or indirect relation with Carbon dioxide emission.
When it comes to estimate the FE models shown in equation  there are different a priori plausible alternatives. More specifically, there are three important decisions that must be made as regards: (i) the period of time for which we are going to define population and economicgrowth (here we have considered 3 alternatives: one, five and ten year intervals); (ii) the decision to instrument or not to instrument our regressions (2 alternatives), and (iii) the choice between overall population or indicator-specific demographic variables (2 alternatives). Given the uncertainty and arbitrariness involved in such choices, rather than privileging a unique model specification we have preferred to make room for different specifications in the aforementioned areas – therefore resulting in 3·2·2=12 model specifications per MDG indicator[[[Endnote#12]]]. Given the large number of results generated by such approach, we have summarized the main findings in Table 3 (the beta coefficients corresponding to each indicator and model specification are shown in appendix 2). For each cell in Table 3, a ‘+’ (resp. ‘–’) sign appears when all statistically significant betas corresponding to the different models have a positive (resp. negative) sign. When the different betas have positive and negative signs for alternative model specifications, then we display a ‘?’ sign. In addition, we have colored the cells with ‘+’ and ‘–’ signs in green or in red depending on whether or not the sign of the estimated betas goes in the normatively desirable direction (which in turn depends on the scale of the underlying indicator, see row 1). To illustrate: in column 1 (corresponding to the results for I 1 ), the negative betas associated
consumption was almost as important as employment in explaining GDP forecast error variance. Wolde-Rufael (2004) used the Toda-Yamamoto causality test to investigate the causal relationship between various kinds of industrial energy consumption and GDP in Shangai for the period 1952-1999. The study found unidirectional Granger causality from coal, coke, electricity and total energy consumption to real GDP, but no causality in any direction, between oil and real GDP. In their 2005 study, Domac et al (2005) claimed that bio-energy should help increase the economies macroeconomic efficiency through the creation of employment and other economic gains. Later, Awerbuch and Sauter (2006) defended that RES had a positive effect on economicgrowth by reducing the negative effects of oil prices volatility 3 . Furthermore, they contributed to energy supply security. These effects have to be considered when fully assessing the comparative costs of RES and fossil fuels. Ewing et al (2007) used the generalized forecast error variance decomposition analysis to investigate the effect of disaggregated energy consumption (coal, oil, natural gas, hydro power, wind power, solar power, wood and waste) on industrial output in the USA. The authors found that non-renewable energy shocks (coal, gas and oil) had more impact on output variation than other energy sources. Even so, several renewable sources also exhibited considerable explanatory power. Regardless of the sources, energy had always less impact on output variations than employment. In 2008, Chien and Hu (2008) studied the effects of renewable energy on GDP for 116 economies in 2003 through the Structural Equation Modeling (SEM) approach. They decomposed GDP by the “expenditure approach” and concluded that RES had a positive indirect effect on GDP through the increasing
A vast body of knowledge concerning the relationship between populationgrowth and economicgrowth exists in the literature. However, whether a positive or negative relationship exists between the two variables is still unfolding. Nam  asserts that “no firm statements can be made about the relationship between populationgrowth and economic development as different countries have different experiences in this regard i.e. what is true for one country might not be suitable for another country as its populationgrowth pattern and economic structure might be different”. In conformity to Nam’s assertion;  investigated the empirical relationship between populationgrowth and economicgrowth in Malaysia using cointegration and causality analysis. Their findings did not support the existence of long-run relationship between populationgrowth and economicgrowth. Also they found no statistical evidence of causal relationship between populationgrowth and economicgrowth in Malaysia. In a related development, Dawson and Tiffin  analyzed the long-run relationship between population change and economicgrowth in India using annual time series data from 1950-1993. The study employed cointegration and Granger causality techniques and found no long-run relationship between populationgrowth and economicgrowth. They also found that populationgrowth neither Granger causes economicgrowth nor is caused by it. By employing the same methods of analysis,  found similar results by conducting a similar research using similar variables in seven Latin American countries, namely, Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela using annual time series data for the period 1900-1994. No single long-run relationship between the two variables was found between any of the seven countries. Furthermore, no unidirectional or feedback causality between populationgrowth and economicgrowth was observed.
Economists advocating the positive side to populationgrowth, say that the populationgrowth creates problems in the short run that include poverty, famine and unemployment. Yet, they also state that in the long run, it leads to new developments through advancement in technology that leave countries better off than if the problems never occurred. On the positive side, there is a chain reaction of events caused by populationgrowth. According to the neo-classical growth model, population is beneficial to an economy due to the fact that populationgrowth is correlated to technological advancement. Rising population promotes the need for some sort of technological change in order to meet the rising demands for certain goods and services. With the increased populace, economies are blessed with a large labor force, making it cheaper as well, due to its immense availability. An increase in labor availability and a low cost for labor results in a huge rise in employment as businesses are more inclined to the cheap labor. Low labor costs results in a shift of money usage from wages into advancement through technology (Coale and Hoover, 1958).
Carbon emission is one of the leading causes of global warming and climate change. Carbon emission as a causal agent of global warming has received a lot of positive regards from international organizations, governments from different countries and environmental practitioners. To be able to control carbonemissions, relative factors to carbon emission should be adopted in the first instance. Economicgrowth is an imperative factor that impacts carbon emission (Zhu et al., 2016). It has been a scientific consensus that increasing greenhouse gas concentration owning to human activities has dramatically altered the global environment and led to intensifying climate change. Due to the inherent nature of global public good, the control of greenhouse gas (GHGs) has been widely perceived among economists and policymakers as an issue that requires international cooperation across the globe (Chung-Pin et al, 2019). The World Health Organization argues that 18% of global carbon dioxide (CO2) emissions are attributed to energy and to the fuel used by the residential sector. The expansion of greenhouse gas emissions is a serious danger to the environment and on human health. It is estimated that the adoption of cleaner technologies for renewable energy production (solar, wind, geothermal, biogas, etc.) can substantially reduce emissions of climate change pollutants by about 0.4 to 0.9 billion tons of CO2 emissions between 2010 and 2020 (Nicholas et al. 2018).
Pakistan is a developing country in South Asian countries, economy of Pakistan is grow- ing rapidly and it is expected that the economicgrowth of Pakistan will continue with same trend in the future. Pakistan ’ s economy depends on agriculture, and agriculture is the main dominant sector of the country, but due to repaid growth of industrial sector in Pakistan, the agriculture land is cutting. Besides this, rapid increase in population causes deforestation; Pakistan is top ranked country in Asian countries that faces the problem of deforestation. Increase in economicgrowth and industrial sectors use energy for growth that causes environmental degradation. Pakistan is facing high demand of energy for which traditional energy sources are used to meet its fast increasing demand for energy. Wolde-Rufael and Menyah (2010) stated that use of traditional energy resources causes to discharges carbon dioxide that helps to deteriorate environmental quality. Ahmed et al. (2015) stated that environmental degradation affect the environment and health of the human being in Pakistan. Yang and Li (2017) stated that environmental degradation is caused by vast amount of greenhouse gas emissions, including carbon dioxides, nitrous oxide, and methane. Shahbaz et al. (2013) stated that use of fossil fuels for daily life, massive smoke expulsion from the factories and consumption of wood as an energy
In the previous few decades, economic development and its components have been a noteworthy focal point of financial economists, particularly for the creating economies. The investment go about as a power for financial economicgrowth and is the main peak point for an economy, and it tends to be either residential reserve funds or through foreign investment. The foreign investment gives coordinate capital financing that deliver positive externalities, along these lines animate economicgrowth by means of exchange of technology, overflow impacts, establishment of new techniques and methods, and improved administrative abilities (Lee, 2013). To be sure foreign investment adds to the host nation in three different direction, (I) foreign investment fortify accordingly economicgrowth process in the host nation (Alfaro et al., 2010) (II) foreign investment is the wellspring of outer financing (Bustos, 2007) and (III) foreign investment abbreviates the connection between local reserve funds and aim investment (Ndikumana and Verick, 2007).
For each group, it seems very hard to derive an EKC from the nonpara- metric regression, even if apparently the nonparametric estimation for the middle income group displays an inverted-U shape. Indeed, Figure 4 shows that the decreasing part of the curve is not robust since the conÞdence in- terval is very large. Parametric curves for the low and the high income groups (see Figures 3 and 5) have an inverted-U form (EKC) whereas that of the middle (see Figure 4) is monotonous, which is not an EKC. Finally, we observe that the diﬀerence between the nonparametric and the parametric curves is striking for the middle income group: the nonparametric curve Þts the data better than the parametric one, especially for relatively high values of CO 2 emissions.
The paper investigates the role of consumption of both renewable and sustainable energy, as well as alternative and nuclear energy, in mitigating the effects of carbon dioxide (CO 2 ) emissions, based on the Environmental Kuznets Curve (EKC). The papers introduces a novel variable to capture trade openness, which appears to be a crucial factor in inter-regional co-operation and development, in order to evaluate its effect on the environment, The empirical analysis is based on a sample of nine signatories to the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) for the period 1971-2014, which is based on data availability. The empirical analysis is based on several time series econometric methods, such as the cointegration test, two long run estimators, namely the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methods, as well as the Granger causality test. There are several noteworthy empirical findings: it is possible to confirm the U-shaped EKC hypothesis for six countries, namely Australia, Canada, Chile, New Zealand, Peru and Vietnam; there is no evidence of the EKC for Mexico; a reverse-shaped EKC is observed for Japan and Malaysia, there are long run relationships among the variables, the adoption of either renewable energy, or alternative energy and nuclear energy, mitigates CO 2 emissions, trade openness leads to more beneficial than harmful impacts in the long run, the Granger causality tests show more bi-directional-relationships between the variables in the long run, and the Granger causality tests show more uni-directional- relationships between the variables in the short run.
In the Kyoto Protocol the EU committed to reduce its greenhouse gas emissions by 8% in 2012 from its baseline emissions in 1990. In order to fulfill this commitment the EU has established an emissions trading scheme (ETS) in 2005 (see EU 2003a) allowing for EU-wide trade in emissions permits. With respect to emissions control the economies of all member states are split into two sectors. The installations covered by the ETS, referred to as the ETS sector, include combustion installations, mineral oil refineries, coke ovens, installations producing and processing ferrous materials, mineral installations and industrial plants for the production of pulp and paper. In the rest of the economy, called the non-ETS sector (that mainly consists of private households and transportation), emissions control is the national governments’ responsibility and is carried out through instruments other than emissions trading. Another peculiarity of EU emissions control is the existence of emissions or energy taxes in the ETS sectors overlapping with the ETS (Johnstone 2003, Sorrell and Sijm 2003). Table 1 lists exemplarily selective overlapping energy taxes in ETS-sectors. 1
On the other hand, if the government reduces fossil fuel consumption or carries out any policy of energy conversation, it will significantly cause China’s economy to slow down with immediate effect, and its rate will significantly lead China’s economy to fall further, both linearly and nonlinearly, in the future. The empirical findings also suggest that it is necessary to increase sustainable fossil fuel consumption to expand economicgrowth. The lack of smooth fossil fuel supply could become a serious constraint and undermine the pace of economicgrowth. This inference is useful for the government and public policy makers in their consideration of which policies they should choose to reduce fossil fuel consumption or regulate any policy of energy conversation so that economicgrowth will be retarded as little as possisble.
A fourth way for societies to adjust to aging is via increased labor force participation. The reduced fertility that contributes to the shift towards an older age structure allows more women to enter the labor force, as highlighted in Bloom, Canning, Fink, and Finlay (2007) 25 , which can potentially compensate somewhat for the retirement of the elderly. We show the magnitude of these effects in Exhibit 22. The bottom row of the exhibit shows the actual labor force to population ratios in 2000 for females only (column 1), and the total population (column 2), respectively. In the upper part of the table, we show the predicted values of female and total LFTP between 2000 and 2040. As in Exhibit 19, we keep age- and gender-specific participation rates constant at their 2000 levels and impute the resulting LFTP in 2040. While the results displayed in Exhibit 19 are exclusively based on the medium-fertility population scenario, we now also show results for the alternative low- and high-fertility scenarios. 26 As can be seen in the upper part of Exhibit 19, fertility rates critically shape the LFTP in 2040. Under the low-fertility scenario, the fraction of workers to the total population goes up from 0.465 in 2000 to 0.509 in 2040. Under the high-fertility scenario, the fraction slightly declines to 0.464. Higher fertility rates imply higher future youth dependency ratios and thus lower expected LFTP in 2040. This difference is even more pronounced when behavioral changes are taken into account. In the second part of Exhibit 22, we calculate “counterfactual” female labor force participation under the assumption that female labor supply responds to changes in fertility. Bloom et al. (2007) 27 estimate the labor supply response for each age group; we use their point estimate and then calculate female labor force participation based on the original 2000 rates plus the estimated adjustment to changing fertility rates. Under the low-fertility scenario, this behavioral response is largest, leading to an increase in LFTP from 46.5% in 2000 to 53.6% in 2040. Under the high- fertility scenario this effect is less pronounced, but still implies an increase in LFTP relative to 2000, rather than the decrease expected in the absence of behavioral change.
The pursuit of economicgrowth worldwide by human beings has resulted in rapid urbanization, industrialization and agricultural operations that are responsible for environmental degradation and pollution (Kijima et al., 2010). Reductions in environmental degradation and pollution are a sine qua non for sustainable development of any economy (Khan and Ullah, 2019). Among the greatest challenges facing humanity in the 21st Century is the extensive environmental degradation and pollution induced by increasing greenhouse gas emissions. Existing literature, (for example, those produced by Khan and Ullah 2019; Haigh, 2017; Li and Yang, 2016; Ramachandra et al. 2015; Abas and Khan, 2014; Gholipour, 2013; Karl and Trenberth, 2003), suggests that that greenhouse gases (GHG) emissions underpin global warming and climate change. GHGs comprise; carbon dioxide, ozone, methane, nitrous oxide, and water vapour. However, carbon dioxide emission is the principal GHG and a major cause for climate change and global warming (Khan and Ullah, 2019). The concentration of carbon dioxide emissions increased by 42% between 1990 and 2014 (Aung et al., 2017). The significant increase of carbon dioxide concentration in the atmosphere is mainly as a result of combustion of fossil fuels, industrial production and large-scale tropical deforestation (Sokolov et al., 2017). Time series on carbon dioxide emissions from fossil fuels go as far back in 1751 (Boden et al., 2011). Over the last 150 years, anthropogenic activities have accounted for almost all the increase in carbon dioxide emissions (O'Neill et al., 2017; Minx et al., 2017).
From an optimistic point of view, the EKC hypoth- esis suggests that economicgrowth is, by itself, the solution to environmental problems in the sense that environmental improvement is almost an inevitable consequence of economicgrowth, and thus, when a country becomes richer, current environmental prob- lems will be addressed by policy changes that not only protect the environment, but also promote economic development (Roca et al., 2001; Perman and Stern, 2003). However, this is a very simplistic conclusion, since environmental degradation is not explained solely by the current emissions rates or pollutant con- centrations, but also depends on past environmental pressures, and as Arrow et al. (1995) conclude, “… economicgrowth is not a panacea for environmental quality; indeed is not even the main issue”.