The Effects of a Revenue-Neutral Child Subsidy Tax Mechanism on Growth and GHG Emissions

Abstract

Growing population, greenhouse gas emissions, and the pressure to improve economic growth are conflicting and controversial issues at the core of political economy. In this paper, using a theoretical model, we show that by shifting relative costs of child-rearing and costs for education, we can achieve a slowdown in population growth and greenhouse emissions, and an enhancement of economic growth. These goals are based on two fundamental considerations—the quantity–quality tradeoff with respect to the choice of the number, and the educational level of children. An analysis is presented using a standard overlapping generation (OLG) framework that is extended with human capital, endogenous fertility, and changing life expectancy. The environmental impact of economic activities is modeled using a modified IPAT framework. Our results show that it is possible to reduce the level of carbon emissions of the whole economy and to generate a Pareto improvement. Subsequently, an economic strategy is presented that is costless, has various advantages, and particularly useful for countries experiencing high fertility rate that are not sustainable.

Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia

“…Besides per capita income and renewable energy solutions, we introduced the life expectancy and population density as determinants of the ecological footprint. In terms of sustainable development growth, these demographic indicators can play a crucial role in the long run (Kumar and Stauvermann, 2019). The literature supports their role in determining CO2 emissions (Abbasi and Riaz, 2016) or EFP (Marquart-Pyatt, 2015;Gazi et al, 2016) in the long run.…”

Environmental Kuznets curve, balanced growth, and influencing factors: evidence from economic development in China

“…Various driver analysis models were used in these studies. Typical models include input-output models (Lin and Xie, 2016;Xie and Zhu, 2020;Yan et al, 2016;Zhang et al, 2017), regressive models (AhAtil et al, 2019;Charfeddine and Kahia, 2019;Khan et al, 2019;Wang et al, 2019c), the computable general equilibrium model (Wu et al, 2019), the IPAT (I = human impact, P = population, A = affluence, T = technology) model (Chang et al, 2018;Dong et al, 2016;Kumar and Stauvermann, 2019;Qu et al, 2017;Song et al, 2018;Tomkiewicz, 2010), the Stochastic Impacts by Regression on Population, Affluence and Technology model (Duan et al, 2020;Shao et al, 2010;Tang et al, 2019), the Laspeyres method (Li et al, 2017;Shang et al, 2016), the logarithmic mean Divisia index (LMDI) method (Wen and Li, 2020;Yang et al, 2020;Zhang et al, 2020), clustering analysis (Wang et al, 2020a), system generalized method of movements (AhAtil et al, 2019) and the EKC model (Al-Mulali et al, 2016;Feng and Ye, 2012;Liu et al, 2018;Rahman et al, 2020aRahman et al, , 2020cYang and Xie, 2015).…”