Research On Modeling Greenhouse Gas Emissions Mitigation Considering Public Participation

Greenhouse gas(GHG)emissions are a serious problem with dangerous environmental consequences for health and climate.Greenhouse gas emission reduction policies encourage the transition to renewable energy consumption by influencing people’s consumption behaviors.However,the social aspects related to energy consumption have not been fully studied.In particular,there is a lack of real-time data on people’s attitudes towards energy taxes and people’s preference for renewable energy.Therefore,analyzing attitudes and preferences towards energy policies provides an opportunity to reduce greenhouse gas emission.Therefore,this research provides a theoretical modeling for the impact of energy taxation,renewable energy and population on GHG emission,analyzing people attitudes towards energy taxation,analyzing people preferences of renewable energy resources and modeling renewable energy adoption.The main solutions in this research are shown below.(1)The study models the impact of energy taxation and renewable energy on GHG emissions under the influence of population demography.First,a theoretical model is established based on the production function.Then a corresponding econometric model is built.The model is applied on a panel of data between 2007 and 2017,on 17 European countries in addition to the USA,Canada and Australia using three regression techniques Pooled OLS,Fixed Effect and Random Effect.Next,a split-sample analysis is performed based on economic development to test the spatial heterogeneity of the policies impact.Finally,a time-varying coefficient test is conducted to test the temporal heterogeneity of the policies impact.The results indicate that energy prices and taxes negatively impact residential and industrial GHG emissions.Labor force has an impact on industrial greenhouse gas emissions depending on wages elasticity.In addition,population size increases residential greenhouse gas emissions,while urbanization reduces them.The study also reveals the regional and temporal heterogeneity of GHG emissions mitigation policies impact.During the study period,the negative impact of taxation and energy prices has increased.(2)The study constructs a model to analyze people’s attitudes towards carbon taxes and discover the driving factors of these attitudes.Firstly,a samples of carbon taxesrelated tweets is collected from Europe,the United States,and Australia.Then,keywords that symbolize people’s attitudes are extracted based on word frequency and centrality.Then,the bisecting k-means algorithm and correspondence analysis are used to cluster the tweets based on these keywords.Word collocation,frequency and centrality are used to discover the main driving factors of people’s attitude towards carbon tax.Correspondence analysis shows the importance of each factor.Finally,sentiment analysis is performed to measure people’s attitudes toward carbon taxes.The results show that the main driving factor of people attitudes toward carbon taxes are trust in government,education,and perceived costs of individuals and businesses.Sentiment analysis reveals the negative sentiment towards carbon tax in most research countries.(3)The study constructs a model to analyze people’s preferences for renewable energy resources.Samples of tweets are collected from Europe,the United States and Australia.Firstly,a network of tweets is built based on semantic text similarity.Then,the label-propagation algorithm is used to detect discussion topics in the network.Sentiment analysis is used to measure satisfaction with energy,and mention rate is used as a measure of interest.Finally,satisfaction and interest are compared to identify development opportunities for renewable energy policies.The results show that the government influence on Twitter is less than the influence of private renewable energy suppliers.From the perspective of interest,people’s interest in renewable energy is slightly increasing.Solar and wind energy have the highest interest.Interest in geothermal energy is declining,while interest in biomass energy is increasing.From the perspective of satisfaction,biomass energy has the highest satisfaction.Therefore,if people’s satisfaction with solar and wind energy is improved,they may become the two most promising energy sources in the future energy transition.(4)A novel interactive model is developed to simulate the renewable energy diffusion.The model is built using delay differential equations based on population theory.First,the boundedness of the model and the uniqueness of the solution are analyzed.Then,the equilibrium points are identified and a stability analysis is conducted.Then,a numerical simulation is performed to find the optimal parameters of renewable energy diffusion.Different scenarios were discussed and tested.The results show that energy taxation and renewable energy subsidies have interrelated effects.Technical support and information exchange are essential for successful adoption of green energy.In addition,the delay in the adoption of renewable energy greatly influences renewable energy diffusion.This study contributes to the formulation of energy policies and provide additional insights for environmentally sustainable development.It points out the importance of social media as a source of data for energy policy input and proves the need for better citizen consultation and interest assurance before introducing GHG mitigation policies.The study implies that governments should practice a higher influence on promoting awareness of the environment,converge between people’s interests and feasible energy solutions,and facilitate technical information exchange to speed energy transition.