Background: The current research attempts to systematically investigate the causal interactions between renewable energy generation, aggregated energy use, human capital, and economic performance in Pakistan both in a short-term and long-term test for the period of 1990–2016. Methods: As a primary step, a unit root analysis was conducted employing, among others, an augmented Dickey-Fuller-generalized least squares (ADF-GLS) test. Based on the order of integration I(1), the Johansen and Juselius (JJ) co-integration testing was employed to confirm a long-term causality analysis, which was followed by a vector error correction model (VECM) to calculate the short-run Granger causality analysis. Furthermore, the vector autoregressive (VAR)-based Cholesky test allowed the standard deviation impulse response functions to be generated to explain the responses of variables to arbitrary shocks in the data series under analysis. Results: The empirical findings unearthed the bilateral causal connection between aggregated energy use and economic performance, renewable energy generation and economic performance, and human capital and economic performance. Thus, it confirmed the existence of feedback effects for aggregated energy use, renewable energy generation, and human capital in their relation to economic performance. Likewise, a unilateral positive causal connection was revealed running from renewable energy generation and human capital to aggregated energy use, and from human capital to renewable energy generation in both a long-term and short-term test. Additionally, the causal association running from aggregated energy use and renewable energy generation to economic performance was exposed in a long-term as well as short-term test, hence supporting the growth hypothesis. Conclusions: The findings signified the importance of an enhanced generation of renewable energy along with the promotion of an aggregated energy use for the economic performance in Pakistan.

In 2020, China proposed the goal of achieving carbon emission peaks by 2030 and carbon neutrality by 2060. For China, whose energy consumption structure has long been dominated by fossil energy, carbon trading and new energy are crucial for the realization of the emission target. By establishing a connectedness network model, this paper studies the static and dynamic spillovers between the Hubei carbon trading market, new energy stock market, crude oil market, coal market, and natural gas market in China, and draws the following conclusions: (1) the static spillover index of the carbon–energy–stock system is 3.57% and the dynamic spillover index fluctuates between 7.67% and 22.62%, indicating that the spillover effect of the system is low; (2) for the whole system, whether from a static or dynamic perspective, the carbon market always plays the role of net information receiver, while new energy is the net information transmitter; (3) the new energy stock market and the coal market always act as net information transmitters to the carbon market; and (4) the spillover effect of the system is asymmetric, wherein the system is more sensitive to negative information about price returns, and this asymmetry is much greater when the system is active.

Since employing environmentally friendly technologies (EFTs) is a complicated and difficult process that is affected by multiple factors, researchers are encouraged to explore those influence factors. This research aims to estimate the households’ perception factors affecting the adoption intentions of EFTs. Based on a questionnaire survey from 782 Pakistani households, a structural equation modeling strategy yields empirical results. The main findings are: firstly, the drivers of adoption intention of EFTs include technological awareness, perceived environmental importance, perceived behavioral control, and perceived benefits. Secondly, significant barriers to the adoption intention of EFTs involve the perceived cost of EFTs and the perceived risk-averse behavior of households. Thirdly, among all factors, perceived environmental importance reveals the most substantial contribution, whereas perceived risk-averse behavior shows the least contribution to the adoption intention of EFTs. Finally, given these results, strengthening the financial benefits, controlling the roaring cost of EFTs, and the provision of rebates and subsidization are suggested to upscale the adoption intention of EFTs by the households.

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

Under the carbon trading mechanism, renewable energy projects can gain additional benefits through Chinese Certified Emission Reduction transactions. Due to the uncertainty of carbon trading system, carbon prices will fluctuate randomly, which will affect the investment timing of renewable energy projects. Thus, the value of the option will be generated. Therefore, renewable energy power generation project investment has the right of option. However, the traditional investment decision-making method can no longer meet the requirements of renewable energy investment in the current stage. In this paper, a real option model considering carbon price fluctuation is proposed as a tool for renewable energy investment. Considering optimal investment timing and carbon price, the model introduces a carbon price fluctuation as part of the optimization, studies the flexibility of enterprises’ delayed investment under the fluctuation of carbon price. A case study is carried out to verify the effectiveness of the proposed real option model by selecting a wind farm in North China. The model is expected to help investors to assess the volatility and risk of renewable energy projects more accurately, and help investors to make a complete plan for the project investment, thus promoting the efficient allocation of resources in the energy industry.