Explore the vast range of titles available.

Global climate change and growing water demand exacerbate the imbalances in reservoir resource allocation, necessitating advanced frameworks that move beyond static valuation methods. Traditional valuation methods, constrained by static or homogenized assumptions, fail to capture the spatiotemporal heterogeneity and dynamic trade‐offs inherent in cascade reservoir operations. To address this gap, this study develops a novel marginal value framework based on shadow pricing to assess the marginal values of multidimensional resources—water, storage capacity, and turbine capacity—in weekly hydropower scheduling. The key methodological contribution is overcoming the duality gap challenge in mixed‐integer linear programming (MILP) models by proposing two practical methods: Method‐I fixes integer variables to derive dual multipliers via linear programming, while Method‐II computes shadow prices through perturbation analysis. Validated on 26 cascaded reservoirs in Yunnan, China, both methods yield consistent results, with Method‐I demonstrating superior computational efficiency. Key findings reveal that: (a) irrigation water values in dry seasons and upstream regions exceed wet seasons and downstream by 1.11–7.34 times; (b) Nuozhadu Reservoir's storage capacity shadow price peaks in week 44, signaling flood control‐power generation trade‐offs; (c) Wunonglong and Dachaoshan exhibit the highest marginal turbine capacity values for spillage reduction; and (d) reserve capacity costs surge by 32%–45% in weeks 36–37. This work bridges the fields of resource economics and hydraulic engineering, providing actionable insights for dynamic water pricing, infrastructure investment prioritization, and seasonal ancillary service markets.

Climate change poses an urgent threat, necessitating the implementation of measures to actively reduce carbon emissions. The development of effective carbon emission reduction policies requires accurate estimation of the costs involved. In situations where actual prices of commodities are not available in the market, shadow pricing provides a useful method to calculate relative prices between commodities with and without price information. However, most studies focus on the industry, with few contributions on agricultural sector. This paper estimates the shadow price of carbon emissions in the agricultural sector from a provincial perspective, incorporating the impact of livestock into the calculation of carbon emissions and shadow pricing. Our findings indicate that ignoring livestock may overestimate CSP values. On the whole, the level of carbon shadow price is rising, indicating good green development in China’s agricultural sector. The two types of convergence results show that there is sigma convergence and beta convergence in the western and central regions, demonstrating a significant improvement in environmental performance.

PurposeThe greenhouse gas (GHG) emissions caused by the construction industry account for an enormous share of total global CO2 emissions. The numerous construction activities therefore continue to reduce the remaining carbon budget. One lever for the reduction of these GHG emissions lies in the procurement process of buildings. For this reason, a process model was developed that takes embodied and operational emissions into account in the tendering and awarding phase of buildings.MethodsTo validate the developed theoretical framework, environmental life cycle costing (eLCC) was conducted on a single-family house case study, taking into account external cost caused by GHG emissions. Various shadow prices were defined for the calculation of external cost to identify changes in award decisions. We further investigated a results-based climate finance (RBCF) instrument, i.e., the GHG emission bonus/malus, to demonstrate an approach for calculating Paris-compatible cost (PCC) scenarios.ResultsWe show that an award decision based on life cycle costing (LCC) leads to a 12% reduction in GHG emissions. A further reduction in GHG emissions can be achieved by awarding contracts based on eLCC. However, the required shadow prices within the eLCC awards to influence the award decision are quite high. With the development of the LCA-based bonus/malus system, PCC scenarios can be determined at sufficient shadow prices, and further GHG emission reductions can be achieved.ConclusionsSince the implementation of LCA and LCC in the tendering and awarding process is currently not mandatory, in this context, the next step towards Paris-compatible buildings must first be taken by the awarding authorities as well as the policy-makers. However, the application of the LCA-based bonus/malus system and thus the awarding of contracts according to PCC scenarios show the enormous GHG emissions reduction potential and thus represent an innovative and sustainable framework for an adapted procurement process.

Virtual water trade can generate an aggregate value gain or loss when there is a regionally disparity in the value of water resources. This paper proposes a novel integrated model to evaluate the impact of virtual water trade on the gain and loss in both environmental and economic dimensions. Environmentally, when virtual water flows from regions rich in water to regions short of water, the scarcity of water resources at the aggregate level is alleviated and positive gains are obtained. Economically, as virtual water is transferred from economically less developed regions to those that are economically developed, the marginal economic value of water resources is enhanced, resulting in a positive gain. China is characterized by significant disparities in the degree of water scarcity and the level of economic development in different areas of the country. This study therefore focuses on China as a case of how interregional virtual water trade leads to a loss or gain in aggregate value. We employ a Multi-regional Input–Output model to analyze the virtual water flows within China and adopt the Data Envelopment Analysis to evaluate the water shadow price. Results show that the virtual water flow in China in 2015 was mostly from water-scarce to water-rich regions, resulting in a loss of 8 billion m3 of scarce water; however, at the same time, economically developed areas received large amounts of virtual water from less developed areas, thereby generating a net economic gain of 8.5 trillion CNY. In particular, the virtual water trade from Heilongjiang to Shandong yielded the largest of environmental gains, saving 1.65 billion m3 of scarce water, and the virtual water trade from Xinjiang to Guangdong produced the largest of economic gains, hitting 479 billion CNY. This paper aims to serve as an inspiration for regional, national and even global virtual water trade practices.

Green bonds offer substantial positive externalities compared to other types of bonds. This leads to a resource distribution efficiency that falls below the optimal level dictated by Pareto efficiency. It becomes essential to determine a means by which green bonds can achieve an equilibrium price, ensuring optimal public resource allocation and maximized social welfare. From the perspective of externalities, this study employs the carbon shadow price (CSP) to determine the equilibrium price of carbon emissions. Subsequently, this value aids in estimating the equilibrium price of green bonds. Firstly, we introduced an optimized bootstrap method to estimate the bias-corrected CSP at the provincial level in China from 2007 to 2020. Then, a pricing framework is developed, integrating both the carbon trading price and the estimated CSP, to determine the green bond’s equilibrium price. Numerical simulations indicate that, under current conditions, green bonds cannot achieve the equilibrium price by relying solely on the carbon trading mechanism. Therefore, further development of China’s carbon emissions trading market is required.

The shadow price of carbon emissions can measure the marginal output effect under the carbon emission regulation and is also one of the key indicators to construct a low-carbon development path for production units. Currently, international research on shadow price is focused on the industrial and energy sectors. However, in the context of carbon peaking and carbon neutrality targets in China, the use of shadow price to study the cost of reducing emissions from agricultural production, especially forestry fruit industries, is significant. In this paper, we use a parametric approach to construct the quadratic ambient directional distance function. Using the input–output data of peach, we then calculate the environmental technical efficiency and shadow price of carbon emissions from peach production in Guangxi, Jiangsu, Shandong, and Sichuan provinces, and further estimate the values of green output in each province. The results show that (i) the environmental technology efficiency of peach production in Jiangsu province, located in the coastal plain area of eastern China, is the highest among the four provinces, while that in Guangxi province, located in the hilly area of southeast China, is the lowest. (ii) Guangxi province has the smallest carbon shadow price of peach production among the four provinces, while Sichuan province, located in the mountainous area of southwest China, has the largest. (iii) The green output value of the peach production in Jiangsu province is the highest among the four provinces, and that in Guangxi province is the lowest. In order to effectively reduce carbon emissions in peach production without affecting economic benefits, the paper puts forward the following suggestions: for peach-producing areas in the southeast hills of China, it is necessary to increase the application of green environmental technology while reducing the input of production factors in peach production. For peach-producing areas in the northern plains of China, the input of production factors should be reduced. It is not easy for peach-producing areas in the southwest mountains of China to reduce the input of production factors while increasing the application of green technologies. Finally, for peach-producing areas in China’s eastern coastal plain, the implementation of environmental regulations for peach production should be gradual.

Reducing emissions through the invisible hand of the market has become an important way to promote sustainable environmental development. The shadow price of carbon dioxide (CO2) is the core element of the carbon market, and its accuracy depends on the micro level of the measurement data. In view of this, this paper innovatively uses enterprise level input-output data and combines the stochastic frontier method to obtain CO2 shadow prices in China’s industrial sector. On this basis, the impacts of research and development (R&D) intensity, opening up level, traffic development level, population density, industrial structure, urbanization level, human resources level, degree of education, and environmental governance intensity on shadow price are discussed. In further analysis, this study introduces a Spatial Durbin Model (SDM) to evaluate the spatial spillover effects of CO2 shadow price itself and its influencing factors. The research results indicate that market-oriented emission abatement measures across industries and regions can reduce total costs, and it is necessary to consider incorporating carbon tax into low-carbon policies to compensate for the shortcomings of the carbon Emission Trading Scheme (ETS). In addition, neighboring regions should coordinate emission abatement tasks in a unified manner to realize a sustainable reduction in CO2 emissions.

To mitigate the problem of global climate change, governments have taken measures to reduce greenhouse gas emissions. Carbon emission trading has gradually attracted attention as a market-oriented option. Power industry panel data from 30 provinces in China were used for an empirical analysis in this study. The super-efficiency Slack-Based Measure (SBM) model was used to calculate the shadow price of carbon trading and the green total factor productivity (GTFP), and the Ordinary Least Squares (OLS) regression model was used to quantitatively analyze the correlation between the shadow price of carbon trading and the GTFP of the power industry. The results showed that the shadow price of carbon trading had a significantly negative impact on the GTFP of the power industry; therefore, it needs to be improved and perfected. Through a further analysis using the heterogeneity test, it was found that there were problems in the current carbon trading price mechanism. In the face of the above problems, we offer suggestions for improvement from the perspectives of the government and companies. This study helps deepen the understanding of carbon trading prices and the GTFP in the power industry, and it provides a reference for formulating more effective carbon trading policies and corporate green management strategies.

Negative externalities such as nitrogen (N) surplus that accompany dairy production activities are not usually accounted for in the market place since they are not costed. Using a parametric hyperbolic environmental technology distance function approach, we estimate the environmental efficiency and farm-specific abatement costs (shadow price) of nitrogen surplus in dairy farms on the island of Ireland (Northern Ireland and the Republic of Ireland). The methodology, unlike previous approaches (output/input distance functions), allows for asymmetric treatments of production outputs (desirable and undesirable outputs). We also analyse the farm level nitrogen pollution costs ratio and its determinants. The results of our analyses showed that the average environmental technical efficiency estimates for the Republic of Ireland and Northern Ireland are 0.89 and 0.92 and the mean abatement costs per kg of N surplus is €4.02 and €6.2 respectively. We found a reasonable degree of variation in the spectrum of abatement costs across the dairy farms with a relative increase observed over the years.

Evaluating the cost-effectiveness of GHG mitigation in the dryland agricultural sector is needed in terms of designing and implementing detailed and efficient mitigation programs, which is currently rarely covered by the literature. In this paper, we use a parametric directional distance approach to explore the farm-level abatement potential and cost (shadow value) of GHG for dryland farms in southwestern Australia. The study indicates that dryland agriculture could abate substantial GHG emissions and save agricultural inputs simultaneously. For the years 2006–2013, the average abatement potential ratios fluctuated between 17 and 33%, with a mean value of 21%. The mean shadow price of dryland agricultural GHG was $17.60 per tonne CO 2 -e in 2013 Australian dollars. In general, the analysis supports that reducing GHG in dryland agriculture is relatively cost-effective.