This research examines the impact of green marketing and its factors on purchase intention through green brand trust and image. It examines the correlation between six concepts: green knowledge, environmental attitude and knowledge, green image, green trust, and purchase intention. An empirical survey was used to test the hypotheses. Data were collected from a total of 300 respondents from Pakistan, and structural equation modeling (SPSS-AMOS) is proposed to assess the relationships of the research model. The findings show a significant positive association between practical green marketing approaches (green knowledge, attitude, environmental knowledge, and purchase intention for green products); additionally, green products with high ecological qualities create a positive intention towards consumer choice. Furthermore, green marketing approaches (green knowledge, attitude, and environmental knowledge) directly and significantly influence green image and trust. This study suggests that green approaches can be used as a brand marketing strategy to enhance customer purchase intention. The study also found that green marketing can help brands achieve and maintain brand equity. This research explores the relationship between the multidimensional green marketing approaches (green knowledge, attitude, and environmental knowledge) to enhance the purchase intention of green products; the creation of an overpowering market brand was one of its primary objectives. This study offers managerial implications for the development of green firms.

Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.

Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

With the rapid development of its national economy, China has become a major producer and consumer of energy. To guarantee the sustainable development of power industry and national economy, China should exploit fossil and renewable energy efficiently according to the development situation of generation resources. Firstly, this paper analyzes the utilization status of main generation resources in China, such as coal, hydropower and wind energy. Secondly, this paper illustrates the STEP model, which analyzes some issues for China’s generation resource utilization from political, economic, social and technological aspects. For example, the resource distribution is inconsistent with electricity demand, the renewable energy power output is intermittent, and there is some disruption in coal mining. Finally, combined with the utilization status and issues, this paper presents some improvement approaches from the perspectives of cost, efficiency and external influence.

This paper presents a macroscopic view on the prevailing policy and potential issues in respect of the sustainable wind power development in China. It starts by analyzing the characteristics of wind power resources and pin-pointing the relationship between the regulatory policies and various economic, taxation, legal and grid integration attributes relating to the wind power development. Then it follows by analyzing the status quo and capabilities of the wind power manufacturing industry in China including its operational efficiency and grid integration standards. The economic and environmental benefits are estimated by relating to the associated costing analysis in respect of the major contributing factors such as manufacturing, operational and financial factors. Results of the associated benefits analysis indicate that the use of the wind power generation helps to save a significant equivalent amount of standard coal consumption resulting to reduction of emission effectively. Finally, the potential of wind power development in China is shown to be affirmative and the sustainable energy policy is effectively implemented in China.

The adoption behavior of electric vehicles (EVs) has received considerable attention. However, the whole process of purchase behavior has not been well understood. In response, this paper investigates the uptake of EVs in Beijing, China, with a focus on the whole process of purchasing an EV, as well as the relevant policies. Specifically, the classical Howard–Sheth theory is used to the analyze the whole process of EV purchasing, which is divided into four stages here, namely, “Ignored and Neglected”, “Proactive Attention”, “Comparison and Selection of Vehicles” and “Usage Evaluation”. Furthermore, several “what-if” scenarios are set up to quantify the influence of some typical EV-related polices, including the license plate lottery policy and no traffic constriction on battery electric vehicles (BEVs). The results suggest: (1) 63.4% of BEV owners have rigid travel demands with cars; (2) BEVs with a driving range of 500 km, 30-minutes charging time and price of RMB 15,000 are the first choices for the majority of people; (3) 64% of BEV owners are influenced by positive comments from the users around them in the adoption of EVs; (4) the no traffic constriction on BEVs tends to be more influential than the license plate lottery policy in terms of the numbers of applications for the vehicle purchase permits and BEV purchasers, as well as energy saving and vehicular emission reduction. These findings should be helpful for different EV-related stakeholders, such as the government, to shape their policies and promote the development of EVs.