This book introduces the basic tools of dynamic optimization in economics to study environmental problems, applies econometric methods to estimate and test the models derived by dynamic optimization, and discusses environmental problems in a broad perspective, including the design and implementation of environmental policies. Although the coverage is selective, it represents what the author has to offer from his perspective and experience gained in research in dynamic optimization, econometrics and policy analysis, especially for China.
The volume is self-contained for readers with mathematical background of first-year graduate students in the analytical fields of science and engineering but only limited training in economics, while an economics text presumes more knowledge of economics. Once the tools are mastered, the reader can pursue his own research on the topic if he is interested, or simply become a more mature citizen in the global economy.
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Contents:Environmental Economics: Basic Ideas and Analytical MethodsChina's Energy and Environmental Problems and PoliciesOptimal Path for CO2 Emission to Control Global WarmingMacroeconomic Models Incorporating the Effect of PollutionStochastic Models to Study the Effect of Climate ChangeParameter Uncertainty in Models of Global WarmingRegional Differences in Environmental PoliciesMacroeconomic Models to Explain Pollution and Environmental ProtectionUse of Emission PermitsEnvironmental Kuznets CurveClean Energy and International Efforts to Solve Environmental Problems
Readership: Academics, professionals, graduate students involved in the environmental economics.
Key Features:This book gives an insight to researchers outside of Korea on why Korean students at elementary and secondary levels consistently achieve high scores in TIMSS and PISA for mathematicsThe book, being in English, will address a wider group of readers globally, particularly to educational theorists and practitioners of mathematics education, as such literature is restricted to a limited amount of papers