Shadow Prices Research Articles

True Costing in Logistics & Supply Chain Management: How do we make decisions based on True Economic Trade-Offs (T-ETOs)?

Decision-making in logistics (including /supply chain management) is often based on traditional cost-price information from a company's accounting department. Externalities, like social and environmental impact, are usually not included in decision-making. To include a more integrated trade-off, the cost-price information should include information on the traditional costs and the costs of externalities like fair wages (social costs) and costs of damage, pollution, etc. (environmental costs). The article provides an overview of traditional costs and attempts to monetise externalities (by using the concept of shadow prices and the Lagrange multiplier or λ) as a base for decision-making in logistics. Some case studies are presented from the last decade and an example of a true economic trade-off for buying a diesel truck-tractor or an electric truck-tractor. In the previous example, much decision data is missing because no track record has yet been developed in this industry. The key issue is making external effects measurable so that business practices can make sound decisions based on financial, social, and environmental data. The author ends the contribution with a call for further (applied) research into true pricing in logistics.

Measuring the Gains and Losses of Virtual Water Flows in China’s Coastal Areas

The virtual water flow behind product trade provides researchers with ideas to alleviate water problems in China’s coastal areas, with accompanying resource and economic implications. This paper adopts a multi-region input–output model to calculate virtual water flows in coastal areas and analyses resource benefits and economic benefits, by combining the water stress index and shadow price, to measure gains and losses of virtual water flow. This study shows that (1) China’s coastal areas depends on external water supplies; (2) virtual water flows between coastal and inland areas generated 38.26 billion m3 of net scarce water savings and CNY 31,751 billion of net economic benefits, indicating that coastal areas benefited from virtual water flows from both resource and economic perspectives; (3) virtual water flows among coastal provinces and cities caused 11.10 billion m3 of net scarce water losses, but generated CNY 9784 billion of net economic gains, indicating that a further intensification of water stress in coastal areas, but such a virtual water flow pattern was beneficial from an economic perspective. This paper reveals the resource and economic impacts of virtual water flow at the national scale and among coastal provinces and cities and further proposes suggestions for promoting the beneficial flow of virtual water in coastal areas.

Estimating the productivity of US agriculture: The Fisher total factor productivity index for time series data with unknown prices

AbstractIn this paper, we propose a straightforward way to estimate the Fisher ideal total factor productivity (TFP) index (FI) in cases where price information is unavailable, using ‘shadow prices’ derived from data envelopment analysis (DEA). A Monte Carlo experiment shows that the shadow price Fisher ideal TFP index (SPFI) can effectively estimate the ‘true’ FI with relatively small (and stable) errors. The empirical application to the US agriculture sector (1948–2017) further suggests that the SPFI is a (superior) alternative to the traditional Malmquist DEA, especially in dealing with unbalanced panel or time series data when price data are unknown.

The Impact of the Carbon Emission Trading Shadow Price on the Green Total Factor Productivity of the Power Industry in China

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.

Stability and economic performances in the banking industry: The case of China

We estimate stability performances in the Chinese banking industry over the 2007–2017 period using four risk indicators under nonparametric modelling. We are the first to calculate the risk indicator shadow prices, and we use a new way of studying the relationship between stability and economic performance. In particular, we reexamine stability performances when banks achieve their best economic performances. This questions the existence of stability rents, which form a prime reason for the banking authority to consider economic performance. Finally, we verify whether ownership has an impact on our results and investigate the role of the interest rate liberalization reforms.

Co-benefits and influencing factors exploration of air pollution and carbon reduction in China: Based on marginal abatement costs

This study addresses the pressing need for cost-effective emission reduction strategies that maximize co-benefits in terms of air pollution and carbon emissions. Our research contributes to the literature by accurately measuring these co-benefits, thereby facilitating their prompt realization in different regions. We employ an input-output framework that integrates carbon emissions and air pollution, allowing us to calculate marginal abatement costs using the shadow price of undesired output. Through this approach, we quantify the co-benefits and analyze the factors influencing them at both spatiotemporal and factor levels using spatial kernel density and geographical detectors. Our findings reveal several key insights: (1) under joint emission reduction efforts, we observe average annual reduction rates of 6.46% for marginal pollution and 6.10% for carbon reduction costs. Importantly, we document an increase in co-benefits from 0.50 to 0.86, characterized by an initial fluctuation followed by a linear increase. (2) the marginal cost difference for carbon emission and pollution reduction in western China was 179.45 and 155.08 respectively, compared to 321.51 and 124.70 in the Northeast, highlighting the crucial role of regional differences in shaping co-benefit outcomes. (3) we identify a negative spatial spillover effect between provinces, which diminishes over time, leading to heterogeneous effects when local provincial co-benefits exceed a threshold of 0.9. (4) during the adjustment period, we find that the industrial structure exerts significant single and interactive effects on co-benefits. Additionally, we highlight the critical role of environmental governance investment and government intervention as drivers of co-benefits in the current era. By offering the quantification of co-benefits under the marginal abatement costs, our study provides valuable scientific insights for planning and implementing effective synergy strategies.

Evaluation of marginal abatement cost and potential reduction in China's industrial carbon emissions: A quadratic directional output distance function approach

As a relevant indicator for quantifying the value of carbon emissions, the marginal abatement cost (MAC) of carbon emissions has received increasing attention from academics and policymakers. This study uses a quadratic directional output distance function to analyse the technical efficiency, potential emission reduction and shadow prices of China's industrial carbon emissions. The results show that the technical efficiency of China's industry gradually decreased during the sample period. From the perspective of regions with different carbon intensity and industrial output, regions with lower carbon intensity and higher industrial output gradually closed to the optimal frontier for the technology use level and had relatively high technical efficiency. Rising technical inefficiency and carbon emissions led to a potential emission reduction that also exhibits an upwards trend. In addition, the MAC of industrial carbon emissions exhibits a U-shaped trajectory, with 2011 as the turning point. Generally, in recent years, regions with lower carbon intensity and those with higher industrial output have a relatively high MAC for industrial carbon emissions. The empirical findings reveal that a carbon emission reduction policy should be formulated and targeted to each province, and the principle of ‘easy before difficult’ can be followed to promote emission reduction.

Study on China’s 2030 provincial carbon quota allocation scheme considering the positive and negative effects of historical emissions

The carbon quota allocation scheme serves as the fundamental backbone for ensuring the smooth and sustainable operation of the carbon market. Despite numerous prior studies, ongoing debates persist regarding the impact of historical emissions, both positive and negative, on carbon quota allocation. Utilizing the four indicators of historical emissions (both positive and negative), egalitarianism, payment capability, and emission efficiency, this paper employs the entropy method to develop 22 distinct carbon quota allocation plans tailored for China’s provincial regions in the year 2030. Subsequently, utilizing the shadow price method, the study calculates the emission reduction costs of each province under each allocation scheme, thereby evaluating the carbon quota plans from the perspective of emission reduction costs. Finally, a comparative analysis is conducted to assess the impact of both positive and negative historical emissions on the carbon quota allocation scheme, and the paper identifies the carbon quota allocation approach that minimizes the overall emission reduction cost for China. The findings suggest that: 1) allocating more allowances to provinces with higher historical emissions can effectively reduce emission reduction costs for each province; 2) the most cost-effective option for carbon quota allocation is a scheme that takes into account both egalitarian and historical emission criteria; 3) in order to further decrease the overall cost of emissions reduction, it is crucial to achieve comprehensive coverage of the carbon market and facilitate inter-provincial carbon quota trading.

On the palm oil-biodiversity trade-off: Environmental performance of smallholder producers

Oil palm remains an important source of rural income in South East Asia. At the same time, Indonesia has become a hotspot for large-scale species extinction and a loss of biodiversity in favor of agricultural production. The present study sets out to assess the environmental performance of smallholder oil palm production with respect to biodiversity. Using a panel dataset that combines conventional farm data together with an account of plant diversity, we estimate a restricted hyperbolic environmental distance function. We integrate loss of biodiversity as an undesirable output into the production model which allows explaining shortfalls in environmental performance and the derivation of shadow prices of biodiversity conservation. We find a substantial environmental inefficiency, which is partly explained by both chemical and manual weeding practices, highlighting the potential for improvements in both the environmental and the economic dimension. Moreover, the value for conserving one species of the average biodiversity on a farmers plantation was 325 USD in 2018. Payments for ecosystem services schemes could be a viable policy response to conserve meaningful levels of biodiversity while simultaneously allowing smallholders to increase palm oil output. In general, addressing drivers of environmental performance in PES designs amplifies its effect without reducing output.

Stress-induced failure of embodied cognition: A general model

We derive the classic, ubiquitous, but enigmatic Yerkes–Dodson effect of applied stress on real-world performance in a highly natural manner from fundamental assumptions on cognition and its dynamics, as constrained by the asymptotic limit theorems of information and control theories. We greatly extend the basic approach by showing how differences in an underlying probability model can affect the dynamics of decision across a broad range of cognitive enterprise. Most particularly, however, this development may help inform our understanding of the different expressions of human psychopathology. A ‘thin tailed’ underlying distribution appears to characterize expression of ‘ordinary’ situational depression/anxiety symptoms of conditions like burnout induced by toxic stress. A ‘fat tailed’ underlying distribution appears to be associated with brain structure and function abnormalities leading to serious mental illness and poor decision making where symptoms are not only emerging in the setting of severe stress but may also appear in a highly punctuated manner at relatively lower levels of stress. A simple hierarchical optimization shows how environmental ‘shadow price’ constraints can buffer or aggravate the effects of stress and arousal. Extension of the underlying theory to other patterns of pathology, like immune disorders and premature aging, seems apt. Applications to the punctuated dynamics of institutional cognition under stress also appear possible. Ultimately, the probability models studied here can be converted to new statistical tools for the analysis of observational and experimental data.

The allocation of carbon resources in marine capture fisheries.

Marine fishery carbon emissions play a significant role in agricultural carbon emissions, making resource allocation a crucial topic for the overall marine ecological protection. This paper evaluates the dynamic iteration method as a research approach with the factors of resource allocation consisting of value assessment, optimization objective, difference between value assessment and objective, and optimization calculation. The paper selects the shadow price from the Super-SBM model as the judgment function for the goal value, aiming for the fairness criterion. From an equity standpoint, the allocation of carbon resources in marine capture fisheries proves to be unreasonable. The fishery model exhibits an excessive supply of carbon resources, resulting in wastage, while the green fishery model faces a relatively limited supply, with a focus on energy conservation and environmental protection. To address this issue, this paper proposes a new method and discusses the corrective results. This result shows that the stabilization point achieved is a short-term equilibrium rather than a long-term one. By rectifying the social contradiction of profit-oriented approaches, this research provides a fresh perspective for economic studies and applications, particularly in industrial layout and resource utilization optimization.

Food security and the value of game animals—a study of Sweden

The food security value of wild meat is calculated by combining proxy methods for quantifying game animal abundance with shadow pricing techniques for assessing the unit values of food security. This study calculated the food security values of moose, roe deer, wild boar, and fallow deer for Sweden overall and for individual counties. The results showed that meat from these animal populations accounts for approximately 9% of meat consumption in Sweden and for 1.2% of the minimum energy food consumption during periods of crisis for the whole of Sweden, while in some counties it can be as much as 8%. The calculated unit value, or shadow price, of the minimum energy requirement ranged between € 0.1 and € 4.2/mcal, depending on the magnitude of the crisis scenario. At most, the total food security value of actual animal population sizes amounted to 0.50 billion euros, but this was unevenly distributed, with high values in counties that have an abundance of moose and wild boar.

Irrigation water economic value and productivity: An econometric estimation for maize grain production in Italy

Climate change, characterized by rising temperatures and limited precipitation, has intensified the demand for irrigation water while simultaneously restricting its availability. This challenge poses significant risks to agricultural and food production, particularly in the Mediterranean regions where, recently, water deficits have led to substantial production losses and quality issues. Water is a critical determinant of crops' economic viability, especially for water-intensive crops, making it essential to estimate its economic relevance, especially in the absence of reliable water market prices. This study has two primary objectives: first, to evaluate the shadow price of irrigation water for maize grain at the farm level, which is defined as the value generated by the marginal unit of water consumed; and second, to analyse its heterogeneity. Leveraging a Farm Accountancy Data Network (FADN) panel of 1625 Italian farms over a decade (2010–2020), an econometric production function approach is employed. Moreover, quantile regressions reveal variations in the shadow price linked to geographical, managerial, and structural farm characteristics. Our findings underscore water’s key role in economically viable maize grain production, significantly enhancing the productivity of other inputs like fertilizers and pesticides. The average shadow price is 0.29 €/m³, with a median of 0.20 €/m³ and water total productivity accounts for one-third of maize’s average gross output. Quantile regressions uncover how factors like geographic location, altitude, farm management, irrigation water source, and farm size influence the distribution of water productivity, reflecting either efficient use or scarcity of this resource. Our estimation provides valuable insights for policymakers by offering accurate shadow price estimates for irrigation water in Italian maize grain production. Furthermore, it enhances our understanding of irrigation water’s role in the economic viability of this crop, while contributing to support evidence-based water management strategies, identifying vulnerable areas and farms and allowing for future methodological developments.

Aligning opportunity cost and net benefit criteria: the health shadow price.

Given constrained healthcare budgets and many competing demands, public health decision-making requires comparing the expected cost and health outcomes of alternative strategies and associated adoption and financing actions. Opportunity cost (comparing outcomes from the best alternative use of budgets or actions in decision making) and more recently net benefit criteria (relative valuing of effects at a threshold value less costs) have been key concepts and metrics applied toward making such decisions. In an ideal world, opportunity cost and net benefit criteria should be mutually supportive and consistent. However, that requires a threshold value to align net benefit with opportunity cost assessment. This perspective piece shows that using the health shadow price as the ICER threshold aligns net benefit and opportunity cost criteria for joint adoption and financing actions that arise when reimbursing any new strategy or technology under a constrained budget. For an investment strategy with ICER at the health shadow price Bc = 1/(1/n + 1/d-1/m), net benefit of reimbursing (adopting and financing) that strategy given an incremental cost-effectiveness ration (ICER) of actual displacement, d, in financing, is shown to be equivalent to that of the best alternative actions, the most cost-effective expansion of existing programs (ICER = n) funded by the contraction of the least cost-effective programs (ICER = m). Net benefit is correspondingly positive or negative if it is below or above this threshold. Implications are discussed for creating pathways to optimal public health decision-making with appropriate incentives for efficient displacement as well as for adoption actions and related research.

Using Sensitivity Analysis in Linear Programming with Practical Physical Applications

Linear programming currently occupies a prominent position in various fields and has wide applications, as its importance lies in being a means of studying the behavior of a large number of systems as well. It is also the simplest and easiest type of model that can be created to address industrial, commercial, military and other dilemmas. Through which to obtain the optimal quantitative value. In this research, we deal with the post optimality solution, or what is known as sensitivity analysis using the principle of shadow prices. The scientific solution to any problem is not a complete solution once the optimal solution is reached. Any change in the values of the model constants or what is known as the inputs of the model will change the problem of linear programming and will affect the optimal solution. Therefore, we need a method that helps to stand on the impact of changing these constants on the optimal solution that has been reached. General concepts about the binary model and some related theories have also been addressed. By analyzing the sensitivity, we rely on real data for a company that transports crude oil and its derivatives. The mathematical model is formulated for it and the optimal solution is reached using the software. Ready-made sop WINQSB and then calculate the shadow price values for the binding constraints, in addition, linear programming under the fuzzy environment is reviewed, and a new method based on the prime numbers is used to solve the fuzzy parameters model.

A study on the economic loss estimation of industrial wastewater pollution discharges in the Yangtze River Delta urban agglomeration

ABSTRACT This study focuses on the industrial wastewater discharge of 27 cities in the Yangtze River Delta urban agglomeration. It selects five water pollutant indicators from industrial wastewater discharge, including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), petroleum pollutants (PP), and major heavy metal content (HM). A shadow price model for industrial wastewater discharge pollutants in each city of the Yangtze River Delta urban agglomeration is constructed, and empirical research is conducted using government-provided statistical data. The study reveals that the economic losses associated with industrial wastewater discharge in the 27 cities of the Yangtze River Delta urban agglomeration range from 5.9296 billion to 6.448 billion yuan. Shanghai demonstrates the highest economic losses caused by industrial wastewater discharge, surpassing 0.45 billion yuan annually. In contrast, Anqing city experiences the lowest economic losses, approximately ranging from 93 million to 104 million yuan per year due to industrial wastewater discharge.

How does China's energy-consumption trading policy affect the carbon abatement costs? An analysis based on spatial difference-in-differences method

To attain China's target of carbon neutrality by 2060, the implementation of the Energy Consumption Trading (ECT) system serves as an effective approach to improve energy efficiency and mitigate CO2 emissions. Nevertheless, it is crucial to consider not only the importance of carbon reduction effect caused by ECT system but also the associated cost. Therefore, this paper applies the spatial difference-in-differences (SDID) model to assess the direct and spillover impacts of the ECT policy on the carbon abatement cost, which is measured by shadow price of CO2 emissions and calculated using SBM-DEA model. Panel data encompassing 279 Chinese cities from 2010 to 2019 are employed for analysis, with additional insights provided through heterogeneity and mechanism analyses. The results show that ECT policy increases the local carbon abatement cost in pilot cities but decreases the cost of surrounding cities. Meanwhile, such a declination effect in neighboring cities with a spatial attenuation boundary of 1000 km. The heterogeneity analysis suggests that the implementation of ECT policy leads to an increase in the carbon abatement cost in cities located in non-Eastern Coastal regions, cities with abundant natural resources and cities with small populations. The mechanism analysis indicates that the ECT policy implementation facilitates the improvement of green technology in the neighboring cities of the pilot project, consequently lowering the carbon abatement cost. Besides, this policy also results in a decrease in the carbon intensity of the pilot cities. Owing to the U-shaped association between carbon intensity and the carbon abatement cost, the policy implementation stimulates an increase in the cost within the pilot cities.

Investigating the impact of agricultural informatization on the carbon shadow price

The mitigation of climate change requires that agricultural development would proceed in accordance with green and sustainable practices. This implies that economic performance should be improved while minimizing the impact on the environment. One way to assess the underlying costs of sustainable agricultural development, and to model the relationship between the environment and the economy in general, is to use the carbon shadow price (CSP). In this paper, we utilize a by-production model within a non-parametric framework to estimate the agricultural CSP from 1997 to 2020 in 31 provinces of China. The patterns of the agricultural CSP are unveiled through kernel density analysis and convergence tests. Additionally, this paper constructs a comprehensive agricultural informatization indicator using the entropy method. We then empirically investigate its influence on agricultural CSP. The results reveal that China's agricultural CSP has demonstrated consistent growth over the past two decades which suggests serious improvement in environmental performance. Furthermore, the CSP show cross-province variation, albeit with conditional β convergence. Our empirical findings indicate that agricultural information technology contributes to an increase in CSP, thereby indicating its positive role on the green growth of agriculture in China.

Almost Perfect Shadow Prices

Shadow prices simplify the derivation of optimal trading strategies in markets with transaction costs by transferring optimization into a more tractable, frictionless market. This paper establishes that a naïve shadow price ansatz for maximizing long-term returns, given average volatility yields a strategy that is, for small bid–ask spreads, asymptotically optimal at the third order. Considering the second-order impact of transaction costs, such a strategy is essentially optimal. However, for risk aversion different from one, we devise alternative strategies that outperform the shadow market at the fourth order. Finally, it is shown that the risk-neutral objective rules out the existence of shadow prices.

Measuring the carbon shadow price of agricultural production: a regional-level nonparametric approach.

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.