Environmental Regulation: Effects on Economic Growth

Transcription

1 Environmental Regulation: Effects on Economic Growth Tenzin Choephel May 29, 2018 Abstract In this paper I examine the effects of environmental regulation on economic growth. Economic growth decomposition exercises measure a proportion of economic growth attributed to energy use. This value is used as the dependent variable on which multiple countries environmental policy strength is regressed.

2 1. Introduction My research aims to answer how environmental technology regulation impacts economic growth. I am working to create results supported by panel data across multiple countries to approach the issue in a robust manner. In a time where there are arguments against the reality of climate change and the research of climate scientists, I believe that a truly economic approach will target and resonate with a larger audience. This is punctuated by the primarily political discourse around specifically the decision by the United States government to withdraw from recent interational action to combat climate change. In a New York Times article, President Donald Trump is cited as saying, We are getting out. (Shear 2017) This statement was made in reference to the Paris Climate Accords, noting on his part that the deal is bad economically for Americans. The primary motivator I have is the common notion that economic growth has prospered due to a result of fossil fuel use. As non-renewable resource use continues to drain from existing pools, I hypothesize that this non-renewable resource use eventually leads to diminishing marginal returns. Countries with an ability to strongly regulate traditional means of energy use can continue to sustain economic growth by producing and securing renewable resources. This diminishing rate of return would mean that countries might eventually shift away from investment in traditional energy solutions to bolster and sustain economic growth. This would mean that while a country s economic growth may be stifled in the short run by environmental regulation over time the overall economic growth would rebound. Measuring this regulation is done using an environmental stringency index generated by the OECD. This index is then evaluated by its standard deviations to allow us a clearer picture in a relational context. Through increased point values within this environmental index we can see that countries higher along the scale are stronger in adopting environmental technologies and maintaining policies to limit levels of pollution. The adoption and implementation of environmental technology could prove one solution to an dwindling pool of natural resources. 2. Literature Review My project will contribute to the literature by making an empirical analysis of panel data to show the impact of environmental regulation on economic growth. I ground the necessity of the paper in major literature 1

3 by Stern (2006) and Nordhaus (2007) and their opinions on the need to act with regards to environmental regulation. I have looked at a paper by Susmita Dasgupta titled Environmental Regulation and Development: A Cross-Country Empirical Analysis. The paper compares environmental policy and performance for 31 countries. There is strong positive association between environmental development and economic growth shown in his paper, but it is possible this is not causal as the increase may primarily come from a general upward trend in economic growth. Papers in China more closely model the body of literature I aim to contribute to with works done by Zhang,X-P and Cheng X-M (2009) and Long, H., Tang, G., Li,X., and Heilig, G.K. (2007). Zhang writes on China s ability to pursue stronger environmental regulation policy as it would not negatively affect economic growth. Long et.al outlines the changing nature of land use in the Kunshan province of China. Rural and agricultural land use is outpaced primarily by urban development. This land use change coincides with Romer s theory of land as a finite resource and the eventual diminishing marginal returns as a result of reducing land availability. Within these set of papers I aim to show that beyond China s initiatives it is economically beneficial to adopt stronger environmental regulations. 3. Data & Methods The primary data set used in my research is the Penn World Tables. This data set allows for panel data across many countries with values for economic indicators such as the real GDP of each country held at constant national prices for The variables of importance for my paper that I pull are this real GDP value, the capital stock of each country, and the countries total population, which we substitute for labor. The value for Total Factor Productivity used is calculated internally to maintain consistency between each country. TABLE 1: Summary statistics Variable Mean Std. Dev. N EnergyGrowth CapitalGrowth TFPGrowth LaborGrowth GDPGrowth

4 Pulling out simple summary statistics for each of these variables yields a picture in which the majority of countries have very small contributions to total output by energy, Labor, and TFP growth. The majority of output growth on average comes from increases in capital. The values in our table are originally percentages, so the mean of our GDP growth can be interpreted that countries on average see a 4.2% increase The first variable we ll visualize is our rate of GDP growth across our different countries. In this histogram we can see that while some of our countries inhabit the spaces around rates of increases around 20% the majority of our countries are found within the 0-10% column with the peak falling around our mean of 4.2%. 3

5 Energy growth which is our primary explanatory variable in this study is primarily focused between 0 and 10%, but sees a tighter range compared to our GDP growth, though they peak at very similar intervals. Energy growth s distribution is pushed closer to 0 showing that energy growth, which makes up part of our total GDP growth is generally a smaller growth rate. Capital growth, which we could see from our original summary statistic table has much higher values across its distribution. With a mean of 5.7%, capital growth lies distinctly above 0 with the majority of countries seeing some form of capital growth. Our histogram from labor growth is distributed in a smaller set of numbers primarily from -1 to 4%. While 4

6 the numbers don t reach very large values it shows generally positive growth. This is understandable as we substitute values for population to generate labor growth. The picture we see is countries tend to have positive population growth, with a few countries losing population over time. Our final data visualization is of our internally generated TFP growth. This measure of factor productivity follows a similar distribution to our energy and GDP growth histograms. The majority of the data is focused between 1-10% with waning levels as we extend further away Growth Accounting The project consists mainly of a growth accounting exercise in which multiple countries have their output decomposed into multiple growth shares. The process consists mainly of taking a Cobb-Douglas production function in our case denoted by, Y t = (A t L t ) 1 α γ Kt α E γ t (1) and taking the first differences of each variable. Each variable in the equation is then attached an ln prefix to denote the natural log and can be interpreted as the growth rate for each share. lny t = (1 α γ)lna t + (1 α γ)lnl t + αlnk t + γlne t (2) The subscript t attached to each variable indicates the variables are spread out among a time period. Y t 5

7 indicates total output for a country, defined as the real GDP. L t is our variable representing labor, K t is our capital stock and E t is energy use. I primarily aim to find what percentage of output is being generated by energy in the model represented by composite energy use in each country. This variable of energy use is designated in kilograms of oil equivalent per capita. g y = (1 α γ)g a + (1 α γ)g L + αg k + γg e (3) The sum of the growth rates of each variable multiplied by their respective factor shares then equal total output for a country. In this way by sectioning off each one of the growth rates we can regress across each individually to find the impact the environmental stringency index has Regression Analysis The regression models specified below place the different growth shares on the left side of the equation while holding constant environmental regulations in each country. This works to isolate the effect that our environmental stringency index has on each growth share. I also control for levels of Carbon Dioxide to proxy for pollution in a country and GDP to account for differences in wealth. GrowthshareEnergy i,t = β 0 + β 1 EnviroReg i,t + β 2 CO2 i,t + β 3 GDP i,t + +λ i + µ t + ɛ i,t (4) GrowthShareLabor i,t = β 0 + β 1 EnviroReg i,t + β 2 CO2 i,t + λ i + µ t + ɛ i,t (5) GrowthShareCapital i,t = β 0 + β 1 EnviroReg i,t + β 2 CO2 i,t + λ i + µ t + ɛ i,t (6) GrowthShareT F P i,t = β 0 + β 1 EnviroReg i,t + β 2 CO2 i,t + λ i + µ t + ɛ i,t (7) The expected result is that environmental regulation would be affecting growth in energy in our model in greater amounts as the two are hypothesized to be linked closely. 6

8 4. Results While the intial hypothesis was that environmental regulation would have the greatest impact on the energy s share of growth in our model our results show the largest impact in capital. In our table each of the values for our index row are multiplied by the standard deviation of our index. Using an index to demonstrate a change is better explained when we can show a relational effect. One standard deviation change of the index at.962 explains the value shown in our table. (1) (2) (3) (4) EnergyGrowth CapitalGrowth TFPGrowth LaborGrowth index (0.223) (0.274) (0.289) (0.0385) carbon (0.0669) (0.0523) (0.0716) ( ) cons (1.144) (0.990) (1.111) (0.122) N Standard errors in parentheses p < 0.05, p < 0.01, p < The environemntal stringency index does in fact have a large effect on energy s share of growth, though it is negative. This shows a.767% decrease for each standard deviation a country is away on the scale. Capital stock shows a 1.392% increase for each point on the index scale. This can be interpreted as each country increases the stringency of their environmental regulations the value of their capital increases. The implications of this are that while a country may not see increased growth in energy due to higher environmental stringency, they do see an increase in the growth of capital. This growth in capital may have a less meaningful effect however since referring back to our summary statistics, capital is already playing a large part in positively affecting output growth. 7

9 Across all of our growth rates the control variable for carbon is not statistically significant, but does produce low negative values. This could show that increased levels of carbon dioxide in these countries could be negatively affecting the countries growth, but that is a generally inconclusive result. 5. Conclusion The main issues with this paper come from the lack of variables in this panel data set. A more robust determination of environmental aptitude would allow for more countries to be involved in the panel survey. Since the majority of countries reflected in the data are members of the OECD, we face bias in that many of the observations are at similar levels along their growth trajectory. Countries that are not members of the OECD could offer valuable data at showing whether or not fossil fuel use and energy adoption is necessary in the burgeoning stages of a nation s growth. It would also allow us to control for wealth more explicitly. Despite the countries variances in Real GDP they generally exist among a similar grouping of wealth within the world. The set of environmental regulations could also be bolstered by being more indepth. Rather than an index that places countries on a seemingly arbitrary scale where the numbers are purely relational a natural extension would be to pinpoint specific countries and times in which landmark regulations passed. This would allow us to isolate specific times and places where distinct changes happened and allow for us to more clearly demonstrate a treatment effect. 8

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