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An Econometric Study Of The Impact Of Foreign Direct Investment And Energy Consumption On The Environment Of China | 106335
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An econometric study of the impact of foreign direct investment and energy consumption on the environment of China
Joint Event on 3rd International Conference on Ecology, Ecosystem and Conservation Biology & 3rd International Conference on Microbial Ecology & Eco Systems
Emma Serwaa Obobisa, Hai bo Chen, Kofi Baah Boamah, and Michael Wiafe
Jiangsu University, ChinaUniversity of Energy and Natural Resources, Ghana
The massive economic
growth in China in recent
years has generated into
an upsurge in demand for
energy and a tumultuous rise
in carbon dioxide emissions.
Yet, precise estimation of
economic and energy impact
of environmental pollution
remains at the edge of extant
studies. Though several
scholars have struggled
to reveal the main factors
accounting for environmental
degradation; most these
studies utilized common
econometric models such as
vector auto regression and
aggregate the variables, which
for the most part prompts
however contradictory and
mixed results. Thus, there is an
exigent need for precise study
of the economic and energy
efficiency of environmental
degradation whilst applying
strong econometric models
and disaggregating the
variables into its separate
individual variables to
explicate their respective
effects on the environment.
This help to provide robust
results and advances the
debate for better policy
formulation and guidelines
to mitigate carbon dioxide
emissions especially in China.
This study, therefore, seeks to
examine the causal effect of
Foreign Direct Investment and
energy consumption on the
environment of China using a
robust and recent econometric
approach such as Dynamic
Ordinary Least Squares (DOLS)
and bootstrapped Granger
causality. Our study impanels
and tests an ensemble of
a group of vital variables
predominant in recent studies
on environment-energyeconomic
causality: economic
growth, energy consumption,
Foreign Direct Investment,
international trade, and
carbon dioxide emission. Our
study further disaggregated
energy consumption by their
sources to identify their
respective influence on the
environment. Our results
showed that the Dynamic
Ordinary Least Squares
method provide accurate
statistical inference regarding
the direction of the causality
among the variables than the
conventional method such
as OLS and Granger Causality
predominantly used in the
literature as it is more robust
and provide accurate critical
values.
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