Explore the vast range of titles available.

The concept of sharing, enabled by emerging technologies, is playing an increasingly important role in contributing to a transformation toward more sustainable transportation. This study aimed to contribute to the growing body of literature on on-demand transportation services, with a particular emphasis on sharing or pooling a ride when using services such as transportation-network companies (TNCs) and microtransit. The study conducted a shared mobility survey of over 2,500 respondents from selected locales across Texas—ranging from large urban areas to small cities and rural areas. We analyzed the survey data in detail using extensive statistical analysis and inferential techniques and adopted an analysis approach toward implementation-oriented research to address the gap between theory and practice. Demographic, as well as geographic and built-environment, factors were found to play an important role in determining whether users will opt for a shared or pooled service and/or how they perceive these alternatives. The findings highlight the importance of improving safety and security, increasing awareness of the benefits of ride-sharing, and designing appropriate policy measures to promote sustainable mobility. We identified potential operational improvements, government policies, and employer programs to improve shared-ride services and encourage their use, such as reducing uncertainty in shared rides and minimizing inconvenience for passengers. A critical finding was the need to prioritize operational improvements in shared-ride trips over solely relying on financial incentives to induce behavior change. Enhanced public awareness and education were also determined to be crucial regardless of the nature of improvements, policies, or programs that are implemented.

The equipment energy efficiency improvement policy (EEEIP) is one of the important measures of energy conservation and emission reduction in various countries. However, due to the simultaneous implementation of variety policies, the effect of the single policy cannot be clearly reflected. In this paper, a method of identification and evaluation of EEEIP was proposed, and the application was verified by analyzing the example of EEEIP in Japan (Top Runner policy, TRP). Firstly, through the factor decomposition model, this paper studied the energy conservation and emission reduction potential of this policy area in Japan. Then, the TRP was identified by using moving windows and correlation analysis, and the impact of specific equipment in TRP was analyzed. Finally, through the calculation of the rebound effect of the carbon footprint (REC), this paper analyzed the energy consumption and emission reduction effects of TRP in the short-term and whole life cycle. It showed that the policy has a good effect in tertiary industry and transportation, while the effect in residential is poor. For life cycle, the TRP of air conditioning and passenger car can bring better CO2 emission reduction effect, but the emission reduction effect of lighting is basically offset.

The combination of reinforcement learning with deep learning is a promising approach to tackle important sequential decision-making problems that are currently intractable. One obstacle to overcome is the amount of data needed by learning systems of this type. In this article, we propose to address this issue through a divide-and-conquer approach. We argue that complex decision problems can be naturally decomposed into multiple tasks that unfold in sequence or in parallel. By associating each task with a reward function, this problem decomposition can be seamlessly accommodated within the standard reinforcement-learning formalism. The specific way we do so is through a generalization of two fundamental operations in reinforcement learning: policy improvement and policy evaluation. The generalized version of these operations allow one to leverage the solution of some tasks to speed up the solution of others. If the reward function of a task can be well approximated as a linear combination of the reward functions of tasks previously solved, we can reduce a reinforcement-learning problem to a simpler linear regression. When this is not the case, the agent can still exploit the task solutions by using them to interact with and learn about the environment. Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem.

Background This study aims to examine the current policy documents on building healthy cities in China. This will provide ideas for improving these policies and fostering the growth of healthy cities. Methods The NVivo software was used to analyze policy tools and construction areas for healthy city development. Policy documents on healthy urban development issued by central government authorities from 2009 to 2023 were selected. Demand, supply, and environmental policy were the three categories of recognized policy instruments. Health services, health environment, health culture, health society, and healthy people are the five pillars of a healthy city. Results This paper analyzes policy documents on healthy urban development in China from two dimensions. The policy tool is on the X-axis, and the field of healthy city construction is on the Y-axis. Regarding the dimension of policy instruments, supply-based policies were used most frequently, at 62.9%. Environmental policies are implemented 26.5% of the time in conjunction with economic policies. Following this, demand-based policies have a frequency of 10.6%. From the perspective of two-dimensional shapes on policy instruments and healthy city building domains, each of the five fields of healthy city construction focuses on using three policy instruments. From the two-dimensional perspective of policy tools and healthy city building domains, each of the five fields of healthy city construction focuses on using three policy instruments. Conclusion The results show that China’s current healthy city policies use a mix of policies to manage supply, environment, and demand. Most of these policies are based on supply, with high levels of government involvement. However, more policies are needed. They encourage individuals, families, communities, and social groups to participate freely in the policy process. In the future, the combination of policy tools can be quickly adjusted to enhance the effectiveness of policy implementation. At the national level, a strong commitment is made to promote healthy urban development. However, the use of policy tools across different construction sectors needs to be evenly distributed. It must be acknowledged that the effectiveness of policy tools in enhancing efficiency requires further investigation. However, the current study needs to be revised. Differences in the understanding of policy instruments may lead to different results, thus affecting the effectiveness of policy recommendations.

Offline Reinforcement Learning (RL) allows policies to be trained on pre-collected datasets without requiring additional interactions with the environment. This approach bypasses the need for real-time data acquisition in real-world applications, which can be impractical due to the safety issues inherent in the learning process. However, offline RL faces significant challenges, such as distributional shifts and extrapolation errors, and the resulting policies might underperform compared to the baseline policy. Safe policy improvement algorithms mitigate these issues, enabling the reliable deployment of RL approaches in real-world scenarios where historical data is available, guaranteeing that any policy changes will not result in worse performance compared to the baseline policy used to collect training data. In this paper, we propose MCTS-SPIBB, an algorithm that leverages Monte Carlo Tree Search (MCTS) for scaling safe policy improvement to large domains. We theoretically prove that the policy generated by MCTS-SPIBB converges to the optimal safely improved policy produced by Safe Policy Improvement with Baseline Bootstrapping (SPIBB) as the number of simulations increases. Additionally, we introduce SDP-SPIBB, a novel extension of SPIBB designed to address the scalability limitations of the standard algorithm via Scalable Dynamic Programming. Our empirical analysis across four benchmark domains demonstrates that MCTS-SPIBB and SDP-SPIBB significantly enhance the scalability of safe policy improvement, providing robust and efficient algorithms for large-scale applications. These contributions represent a significant step towards the deployment of safe RL algorithms in complex real-world environments.

We investigate the problem of minimizing a certainty equivalent of the total or discounted cost over a finite and an infinite horizon that is generated by a Markov decision process (MDP). In contrast to a risk-neutral decision maker this optimization criterion takes the variability of the cost into account. It contains as a special case the classical risk-sensitive optimization criterion with an exponential utility. We show that this optimization problem can be solved by an ordinary MDP with extended state space and give conditions under which an optimal policy exists. In the case of an infinite time horizon we show that the minimal discounted cost can be obtained by value iteration and can be characterized as the unique solution of a fixed-point equation using a \"sandwich\" argument. Interestingly, it turns out that in the case of a power utility, the problem simplifies and is of similar complexity than the exponential utility case, however has not been treated in the literature so far. We also establish the validity (and convergence) of the policy improvement method. A simple numerical example, namely, the classical repeated casino game, is considered to illustrate the influence of the certainty equivalent and its parameters. Finally, the average cost problem is also investigated. Surprisingly, it turns out that under suitable recurrence conditions on the MDP for convex power utility, the minimal average cost does not depend on the parameter of the utility function and is equal to the risk-neutral average cost. This is in contrast to the classical risk-sensitive criterion with exponential utility.

China has undertaken several major programs of terrestrial ecosystem restoration (ERPs) in recent years, including the Natural Forest Protection Program (NFPP) and the Sloping Land Conversion Program (SLCP). There have been reports on the implementation of these programs, their preliminary impacts, and the problems encountered in carrying them out; a great deal has been learned from these studies. Nonetheless, China's ERPs are not limited to the NFPP and the SLCP. Because a complete documentation and a timely update of these major efforts are still missing from the literature, it is difficult to gauge the scope of these programs and the scale of their impacts. In addition, a more thorough and critical analysis of both the general ERP policy and the specific technical measures used in implementing the ERPs remains urgently needed. The purpose of this article is to tackle these tasks. Overall, with the huge government investments in the ERPs, tremendous progress has been made in implementing them. To complete them successfully and to fundamentally improve the targeted ecosystems, however, it is essential for China to have a more balanced and comprehensive approach to ecological restoration. This approach must include: adopting better planning and management practices; strengthening the governance of program implementation; emphasizing the active engagement of local people; establishing an independent, competent monitoring network; and conducting adequate assessments of program effectiveness and impact.

Chinese-style decentralization has brought about the miracle of rapid economic growth for more than thirty years in China. However, with the implementation of the coordinated development strategy of economy and environment, it has set up obstacles to the decrease in regional carbon emissions. In this study, we clarified the effect of Chinese-style decentralization on regional carbon emissions. Then, we tested the effectiveness of emission reduction policies and their potential emission reduction spaces under the Chinese-style decentralization using the panel data of China’s 12 urban agglomerations. The research results are: (1) Chinese-style decentralization affects regional carbon emissions mainly through factor market distortion, investment bias, and environment regulation. (2) Under the influence of Chinese-style decentralization, the effectiveness of emission reduction policies is not fully realized and shows significant spatial interaction. (3) 1% increase in political decentralization causes an 8.9% decrease in the effectiveness of executive order policy; 1% increase in fiscal revenue decentralization causes a 7.1% decrease in the effectiveness of carbon tax policy; 1% increase in financial decentralization causes a 10.6% decrease in the effectiveness of the cleaner production policy. (4) Collaborative governance of carbon emissions between regions is the direction of future emission reduction, and the redesign of emission reduction policies should be formulated based on the degree of regional synergy.

The paper presents a part of research focused on the definition of circular economy models in the regeneration of existing building stock in the Italian context, identifying policies improvements, strategic partnership and environmental and economic life cycle assessment tools for supporting decision. Through direct-interviews to operators (investors, designers, manufacturers, etc.), the paper analyses the typical relationships and dynamics among them in the Italian building regeneration process. The operators' opinions and requests towards circular strategies (reuse/recycling at building and material levels) are pointed out, in order to highlight the obstacles and levers of circular economy application. The paper shows the strengths and the weaknesses for the regeneration of building stock by the application of circular economy, the opportunities and the threats for circular economy by its application in the regeneration of building stock. In order to achieve circular requalification processes, avoiding waste and enabling practices of reuse and recycling, the change of relationships, policies and business models are defined. Moreover, the paper discusses on the importance of environmental evaluation of circular practices, identifying the decision steps and operators which, with the support of environmental and economic life cycle assessment tools, can select circular strategies towards sustainable requalification process.

In this article we consider the ergodic risk-sensitive control problem for a large class of multidimensional controlled diffusions on the whole space. We study the minimization and maximization problems under either a blanket stability hypothesis, or a near-monotone assumption on the running cost. We establish the convergence of the policy improvement algorithm for these models. We also present a more general result concerning the region of attraction of the equilibrium of the algorithm.