We aim at analyzing the influence of incentive policies on renewable energy deployment in European Union countries and Turkey over the 2000–2018 period. Applying both panel fixed effect and the dynamic panel estimation methods, we compared the results of these two different techniques. Moreover, we included “net metering” that has not been analyzed before, in our policy toolkit, that includes a large number of incentives. Our data cover longer time series than the previous studies in the literature, and it provides new results of different renewable energy policies and drivers for analyzed countries. Empirical findings show that while grants, Research and Development, tax, certification, and policy support have positive and statistically significant impacts on the renewable energy capacity, direct investments, loan, and net metering instruments have no stimulating effect on the renewable energy deployment. These results indicate the deterrent impact of regulations and direct investments on capacity of electricity generated from renewable energy. Moreover, our estimation results reveal that fossil energy consumption, nuclear energy, and GDP are important drivers of renewable energy deployment while net import (energy security) and CO₂ emissions are not. Our study highlights the importance of research and development activities, tax incentives, and efficient policy design in transition to green economy in the countries analyzed. Following the detailed discussion of the results, possible policy implications are presented at the end of the study.

Climate change is bringing drastic changes to the food availability, accessibility, quality, and stability in the world. Pakistan heavily relies on production of wheat for food security. This study investigates the role of climate change on food security over Punjab regions, Pakistan, from 1979–2020. The study utilized the total production of wheat to measure food security. Moreover, the study utilized mean, maximum, minimum temperature, wind speed, and rainfall as a measurement of climate change. This study utilized auto-regressive distributed lag (ARDL) bound test cointegration approach to test the long-run cointegration, while ARDL model is applied to investigate the short-run relationship among modeled variables. This study adopted Augmented Dicky Fuller (ADF) and Phillips Parron (PP) test to check the stationarity of the data. Moreover, the study ensures reliability and validity of the model by utilizing Breusch-Godfrey serial correlation, Breusch-Pagan-Godfry HSK, and Ramsey RESET test. This study found that wheat cultivated area (0.46*), total irrigated area (2.67***), total un-irrigated area (1.93***), and total area sown ([1.0809**) have a positive and significant long-run impact on food production in all regions of Punjab Pakistan. However, while average, the maximum and minimum temperature is negatively and significantly associated with food production in all regions of Punjab (− 1.07**; − 3.33***; − 1.84**), except northern Punjab, where maximum temperature affects positively food production Punjab. Rainfall negatively and significantly affects food production in northern (− 0.34**) and central and southern (− 0.13***) Punjab. Furthermore, wind speed negatively affects food production in all regions of Punjab (− 0.22**; − 0.21*; − 0.11**), except northern Punjab. This study implies that government should develop policies to increase irrigation facilities and loan facilities to increase the total area sown that will help to increase wheat yield and ensure food security. Moreover, the government should devise policies for large-scale plantations to minimize climate change impacts. The study also suggests new improved varieties of the wheat crop that can survive and flourish in the presence of adverse climatic changes, high temperature, and high wind speed.

Incorporating green practices into the manufacturing process has gained momentum over the past few years and is a matter of great concern for both manufacturers as well as researchers. Regulatory pressures in developed countries have forced the organizations to adopt green practices; however, this issue still lacks attention in developing economies like India. There is an urgent need to identify enablers of green innovation for manufacturing organizations and also to identify prominent enablers among those. This study is an attempt to first identify enablers of green innovation and then establish a causal relationship among them to identify the enablers that can drive others. Grey DEMATEL (Decision Making Trial and Evaluation Laboratory) methodology is used for establishing the causal relationship among enablers. The novelty of this study lies in the fact that no study has been done in the past to identify the enablers of green innovation and then establishing the causal relationship among them. A total of 21 enablers of green innovation have been identified; research indicates developing green manufacturing capabilities, resources for green innovation, ease of getting loans from financial institutions, and environmental regulations as the most influential enablers of green innovation. Managerial and practical implications of the research are also presented to assist managers of the case company in adopting green innovation practices at their end.

With the development of China’s economy, the problem of environmental pollution has become increasingly more serious, affecting the sustained and healthy development of Chinese cities and the willingness of residents to invest in fixed assets. In this paper, a panel data set of 70 of China’s key cities from 2003 to 2014 is used to study the effect of environmental pollution on home prices in China’s key cities. In addition to the static panel data regression model, this paper uses the generalized method of moments (GMM) to control for the potential endogeneity and introduce the dynamics. To ensure the robustness of the research results, this paper uses four typical pollutants: per capita volume of SO₂ emissions, industrial soot (dust) emissions, industrial wastewater discharge, and industrial chemical oxygen demand discharge. The analysis shows that environmental pollution does have a negative impact on home prices, and the magnitude of this effect is dependent on the level of economic development. When GDP per capita increases, the size of the negative impact on home prices tends to reduce. Industrial soot (dust) has the greatest impact, and the impact of industrial wastewater is relatively small. It is also found that some other social and economic factors, including greening, public transport, citizen income, fiscal situation, loans, FDI, and population density, have positive effects on home prices, but the effect of employment on home prices is relatively weak.

Coal-fired power will remain China’s primary source of electricity for a long time to come. The clean development of coal-fired power generation has become an important strategic choice for China’s energy transformation. Based on collecting and sorting out the driving policies for China’s clean development of coal-fired power generation from 1997 to 2016, firstly, we reviewed the historical evolution of these policies in terms of the policy quantity, policy theme, and policy instrument. Then, we designed the scoring criteria to quantify the policies. Finally, the effectiveness of the overall policies and different policy instruments was empirically analyzed by the econometric model built on the basis of Cobb-Douglas production function. The results show that the annual release amount of driving policies for the clean development of coal-fired power in China presents a trend of fluctuating growth. Policy themes are mainly divided into two parts, namely production capacity management and air pollutants control; policy instruments mainly include elimination of backward units, project approval, standards, generation price regulation, tax incentives, loan policy, and information publicity; the overall policies have significant positive effects on the clean development of coal-fired power industry; in terms of policy instruments, the elimination of backward units and generation price regulation have significant effects on improving the energy efficiency; project approval and generation price regulation are effective for reducing SO₂ emissions rate; and the effects of standards, tax incentives, loan policy, and information publicity on the clean development of coal-fired power are not significant. On these bases, some policy implications to further promote the clean development of coal-fired power industry are provided.

The purpose of this study is to explore the effect of financial development on CO₂ emission in 129 countries classified by the income level. A panel CO₂ emission model using urbanisation, GDP growth, trade openness, petroleum consumption and financial development variables that are major determinants of CO₂ emission was constructed for the 1980–2011 period. The results revealed that the variables are cointegrated based on the Pedroni cointegration test. The dynamic ordinary least squares (OLS) and the Granger causality test results also show that financial development can improve environmental quality in the short run and long run due to its negative effect on CO₂ emission. The rest of the determinants, especially petroleum consumption, are determined to be the major source of environmental damage in most of the income group countries. Based on the results obtained, the investigated countries should provide banking loans to projects and investments that can promote energy savings, energy efficiency and renewable energy to help these countries reduce environmental damage in both the short and long run.