地理邻近与知识产权——基于中国1 331项新能源技术发明专利的实证研究

虽然远程交流技术快速发展，但现有研究表明专利发明在很大程度上仍依赖于信息的地理邻近。以中国1992-2009年的1 331个新能源发明专利为研究对象，运用Jung Wonn,Jaffe等的实验设计方法，研究了专利引证的地理邻近是否会随时间递进而增大。结果表明，个人、大学、公司及其它种类发明专利引证的地理邻近会随着时间变化而增大，而政府受到的影响较小。另外，由于区域城市分属不同省份，因此并不能证明区域专利引证的地理邻近也会随着时间变化而增大。     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

Existence of long memory in crude oil and petroleum products: Generalised Hurst exponent approach

This study examines the presence of long-run dependence in a variety of crude and refined energy spot markets during the 1986–2018 period using the time-varying generalised Hurst exponent. Our results indicate that the weak-form efficiency in energy spot markets is clearly time-varying, with USGC(U.S. Gulf Coast Conventional Gasoline) Diesel Fuel the most efficient and Propane the least. An important finding is that after the subprime crisis, the persistence of energy spot market products has increased. Overall, our finding highlights that the time-varying model is preferable to the time-constant one since the former can capture time-varying efficiency, which heavily depends on a country’s predominant economic and political conditions.     

Testing the transport-induced environmental Kuznets curve hypothesis: The role of air and railway transport

The airline and railway industry contribute immensely to economic development, however, its role in environmental pollution requires attention. Here, this study builds on the theoretical pedigree of the environmental Kuznets curve (EKC) hypothesis to explore the contribution of the air and railway transportation sector and urbanization to the emission-growth argument. We utilized annual time-frequency data from 1995 to 2014 for a panel of top 10 air passenger carrier countries using robust panel estimators that control for cross-section dependence. The empirical analysis shows a positive significant relationship between emissions and economic growth, thus, economic growth is emission-embedded with limited green growth. The existence of the EKC phenomenon is affirmed for the investigated blocs — where economic growth is prioritized over environmental quality. Additional, while air transportation drives pollution, railway transportation and urbanization decline emission over the sampled period. The results underscore the need for clean and environmentally friendly energy sources for air sector operations.     

How to reduce energy intensity to achieve sustainable development of China's transport sector? A cross-regional comparison analysis

China's transport sector has been attracting great attention for its excessive energy consumption and ever-increasing pollution emissions. Thus, reducing energy intensity is one of the top priorities of China's ongoing transport upgrade. In this paper, by establishing a panel data regression model derived from the Cobb–Douglas cost function, we focus on investigating the impacts of energy price and transport productivity on transport energy intensity at the national and regional levels. The study uses the provincial panel data for 2005–2016 to perform regression analysis. The results show that: (1) energy price has a significantly negative effect on transport energy intensity in the whole China and the eastern region, whereas it has no significant impacts in the central and western China. (2) Improvements in transport productivity can effectively decrease transport energy intensity in the whole China and the three major regions. (3) Applying an extended data envelopment analysis (DEA) approach, we decompose transport productivity into four components (i.e., technical change, technology gap change, scale efficiency change, and pure efficiency change) and further differentiate their impacts in different regions. The results indicate that these four components have substantially different impacts in each region. These results provide some valuable insights for policymakers and enterprise entities aiming to adopt measures to reduce energy intensity and achieve sustainable development in China's transport sector.     

《能源宪章条约》下可再生能源投资仲裁案及启示——以西班牙投资仲裁案为主线的考察

为了加速可再生能源产业的发展,多数国家都制定了相关激励措施以吸引跨国公司的投资。但是,随着政策的变化,相关的法律争端也随之产生。除了国际贸易争端以外,与可再生能源相关的投资争端也大量出现,其中以西班牙最为典型,所涉投资案件也最多。随着"一带一路"倡议的推进,我国投资者在海外可再生能源领域的投资会大幅增加。与此同时,我国能源领域对外开放的幅度正在加大,可再生能源领域的外资也会越来越多,但我国现有法律制度并不足以规范可再生能源的未来发展。本文分析西班牙相关法律政策变化及仲裁庭的裁决,以期对我国可再生能源法律政策的发展和完善,以及可再生能源领域的海外投资者保护提供启示,也为我国分析评估是否加入《能源宪章条约》提供借鉴。     

International sanctions’ impact on energy efficiency in target states

This paper investigates the impact of international sanctions on energy efficiency by employing the panel fixed effect as well as average marginal effect from the Tobit model via data on 30 sanctioned states over the period 1996–2015 with international sanctions including unilateral, plurilateral, U.S., EU, UN, economic, and non-economic cases. Overall, we find that the imposition of unilateral sanctions leads to a 0.067% decrease in energy efficiency, but that of plurilateral sanctions positively contribute to energy efficiency in the case of the full sample of countries. Moreover, the imposition of UN sanctions has a greater decrease on energy efficiency in the target states than the 0.042% reduction of energy efficiency when the sanctionist is the U.S. For robustness, empirical results indicate that the imposition of plurilateral sanctions results in a drop of energy efficiency in Islamic countries, but an increase in non-Islamic countries, while there are also negative shocks induced by the imposition of EU sanctions on energy efficiency in Asian countries, but not for non-Asian countries. We also consider endogenous problems and dynamic specification by using indicator variables and System GMM. In summary, our empirical findings provide policy suggestions for those sanctioned countries about how to maintain energy efficiency when facing international sanctions.     

A typology of actors and their strategies in multi-scale governance of wind turbine conflict within forests

Wind turbine construction, governed by complex multi-scale governance systems, can cause conflict between actors interested in forest management. We examined wind turbine conflicts in forests in two case studies, the state of Maine, USA and the state of Rhineland-Palatinate, Germany. Specifically, we examined based on triangulation of documents, participant observation, and qualitative interviews with 46 actors how actors argued in favor of certain scales within the wind turbine multi-scale governance systems and how they applied scalar strategies within them. In this paper, we propose a typology that describes actor behavior within multi-scale governance systems. Seven different actor types within multi-scale governance were identified: the Knowledge Exchanger, the Linker, the Creator, the Maintainer, the Power Shifter, the Mobile Learner, and the Overwhelmed & Passive. Actors involved in wind turbine conflicts within forests re-shaped and re-defined the governance system by their actions in the conflict process. However, not all actors could equally participate in scalar strategies. Therefore, only some actors had advantages in re-shaping the governance system according to their interests.     

Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric?

Nonlinear, symmetric, and asymmetric dependence characteristics in energy equity sectors matter to portfolio investors and risk managers because of the risks and diversification opportunities they entail. Specifically, nonlinear dependence dynamics between assets are harder to predict, monitor, and manage, and can make investment positions go wrong unexpectedly. In this paper, we investigate whether the dependence dynamics of US and Canadian large-capitalized energy equity portfolios are nonlinear, symmetric, or asymmetric. We draw our results by implementing a robust copula approach based on time-varying parameter copulas and vine copula methods. Both time varying parameter and vine-copula methods indicate that the Canadian energy sector portfolio is driven by nonlinear negative tail asymmetric dependence during the global financial crisis and when the full sample period is employed. On the other hand, it displays nonlinear symmetric dependence during the oil price crisis, implying the need for close monitoring and rebalancing and a more continuous assessment of long investment positions. The US energy sector portfolio is driven by positive tail asymmetric dependence, and by symmetric dependence dynamics during crisis and non-crisis periods.