Socio-economic Planning Sciences最新文献

Solid waste generation continuously puts tremendous pressure on human health, socio-economic and environmental protection, and many regions are transitioning to the circular economy using waste recycling to advance sustainable development. A more practical and integrated solid waste recycling network (SWRN) design is essential for solid waste recycling management, which can be complex and uncertain. Therefore, this paper focuses on the design of a robust SWRN that aims to optimize the construction of sorting centers (SCs) while robustly operating with waste recycling allocation. This approach often involves two main challenges related to the uncertainty of unknown distribution information and the bi-level structure of decision making. To address these challenges, we first present two pairs of uncertainty sets to capture the separation rate and transportation cost in the case of free distribution information. Then, we develop a bi-level framework that integrates SC construction locations and waste operation allocation. For this purpose, a globalized robust optimization bi-level model is developed and reformulated into a mixed integer linear programming. We apply this methodology to the case of Baoding, China to demonstrate its validity. The main numerical achievements show that: (1) the proposed model can hedge the uncertainty in the separation rate and transportation cost with a small price of robustness and provide a robust recovery scheme; (2) the average operating cost of our model for a single period is approximately 19.4% lower than that of the classical robust model; and (3) by adjusting several parameters based on the preferences of waste recycling managers, a balance between operating costs and robustness can be achieved. Finally, some managerial insights are obtained to assist waste recycling managers in solid waste recycling management transition to the circular economy.

In real-world counterterrorism activities, it is usually difficult for the defender and the attacker to accurately know the private information of the each other such as valuations of targets. Instead, players may only know the relative preference on the target valuations from the adversary. In the conflict analysis, graph model is a powerful tool for dealing with relative preferences. This paper studies the defensive resource allocation in terrorism conflict management with incomplete information by establishing a graph model. To solve the model, we divide the conflict states into two types and discuss the conditions under which these two types of states are at equilibrium. Furthermore, we study how the defender should optimally allocate the resource to achieve two goals: (i) achieving a certain Nash equilibrium state desired by the defender; and (ii) minimizing the total loss from an attack in equilibrium. Subsequently, we conduct several numerical analyses: (i) analyzing the effects of both players' investment effectiveness on the optimal defense loss; (ii) comparing our model's results with those obtained using three classical decision methods, revealing that the defense loss in our model is lower; and (iii) presenting a case study to illustrate the applicability of the proposed model. This paper provides novel insights on how to efficiently allocate defensive resource when the defender and attacker know only the relative preference of the adversary on target valuations.

To address the residency training performance and further explore its determinants, with the help of a unique dataset, our study calculated the efficiency of residency training and health outcomes in 18 Chinese tertiary hospitals from 2020 to 2021 using a two-stage data envelopment analysis (DEA) model given the two-stage characteristics of vocational training and clinical practice of residents. The results showed that the efficiency of the sample hospitals in both residency training and medical service provision was high, there are approximately 1/3 hospitals of sub-efficient in each stage, but the number of efficient units for assessing the residency training performance was slightly less than that for assessing the health outcome performance. All the decision-making units (DMUs) were clustered into four groups through K-means cluster analysis according to efficiency results. The results showed that there was an obvious inconsistency between the teaching goals and the health outcome goals of Chinese public hospitals. In some hospitals, the low residency pass rate resulted in the low efficiency in stage 1, while the redundant inputs in beds resulted in the low efficiency in stage 2. Residency training hospitals should strengthen their synergistic management in programs of residency training and health outcomes.

Equalizing basic public service is crucial for achieving social fairness and justice. Promoting the improvement of public service can be achieved through the improvement of supply efficiency and cross-regional coordinated development. We employed the Super-SBM model, modified gravity model, and social network analysis method to empirically study the spatial correlation network characteristics of supply efficiency of basic public service (SEBPS) based on the data of 34 prefecture-level cities in the three provinces of Northeastern China from 2011 to 2020. The results revealed the overall efficiency of SEBPS in the study area is relatively low, and the supply efficiency is polarized. There is significant non-equilibrium between regions, and the regional difference moment gradually expands over time. There exists a strong spatial correlation between SEBPS in each city, and the overall correlation effect is weak but the network stability has improved. Each city had different roles and statuses in the spatial correlation network, creating a hierarchical structure. The level of geospatial proximity had a positive impact on the spatial correlation and spillover of the network. The level of economic development and infrastructure configuration hurt the network, and there is obvious heterogeneity between cities. The differences in industrial structure, opening up level, urbanization level, population density, the level of fiscal decentralization and technological innovation and personal income level among cities affected the SEBPS network. This study not only provides a new network research perspective and theoretical foundation for public service policies in China's rust belt region but also serves as a reference for urban development in similar regions.

Forest resources provide a good ecological environment for human beings and offer economic well-being and benefits. Value realization of ecosystem products reflects the process from resources to capital. This study investigates the two-stage value realization efficiency of China's forest ecosystem products from 2011 to 2021. A parallel nested network Data Envelopment Analysis model is applied to assess product supply and value transformation stages. In addition, the evolution of the spatial pattern is depicted by a multilayer Standard Deviational Ellipse. The results reveal that (1) the overall value realization trend of China's forest ecosystem products shows a fluctuating growth trend, with an average annual efficiency value of 0.86. (2) The efficiency level of the value transformation stage is lower than that of the product supply stage. (3) Regional disparities persist. The efficiency values in East China and South China are higher than that in other regions. (4) The value realization capacity of China's forest ecosystem products may be closely related to national development strategies and policy orientations.

Timely pre- and post-diagnosis check-ups are critical for various diseases, in general, and for cancer , in particular, as these often lead to better outcomes. Several socio-demographic properties have been identified as strongly connected with both clinical dynamics and (indirectly) with different individual check-up behaviors. Unfortunately, existing check-up policies typically consider only the former association explicitly. In this work, we propose a novel computational framework, accompanied by a high-resolution computer simulation, to investigate and optimize socio-demographic-based Short Messaging Service (SMS) reminder campaigns for check-ups. We demonstrate our computational framework using extensive real-world data from the United States (US) population, focusing on breast cancer. Our results indicate that optimizing an SMS reminder campaign based solely on simple socio-demographic features can bring about a statistically significant reduction in mortality rate compared to alternative campaigns. These results indicate SMS reminder campaigns for pre- and post-diagnosis check-ups can be instrumental in improving healthcare outcomes. However, additional research is needed to bring about applicative tools.

Promoting transition towards new business modes has become a cornerstone of contemporary policymaking. The circular economy approach has provided a novel and interesting paradigm for businesses and decision-makers aiming to boost the sustainability of production and consumption processes. The European Union (EU) has provided a wide legislative framework to guide Member States towards implementing a common set of circular economy (CE) measures and achieving harmonised progress. However, several differences exist among national norms and rules, risking reducing the effective and homogeneous achievement of EU-wide common goals. Based on these premises, following an assessment of convergence processes among Member States, the present research aims to identify factors affecting this process. More in detail, we distinguish between two mechanisms, i.e., convergence driven by international factors or, rather, a consequence of each Member State's decision-making. To do so, we applied a dyadic rare event logit model to a set of 27 EU Member States between 2008 and 2020. Our results show that both channels are in force within the EU when the economic and political consequences of CE policies are considered. Differently, the convergence process is fostered by the identification of “stories of success”, meant as good performance of CE-specific policies implemented in other countries.

In today's competitive business environment, evaluating the performance of decision-making units (DMUs) such as countries and institutions is paramount. Data Envelopment Analysis (DEA) is widely used for this purpose. One prevalent model, the Distance Friction Minimization (DFM) method, is effective in devising improvement strategies for low-efficiency DMUs. However, it has limitations as it only assesses the distance of DMUs to the efficient frontier, neglecting the inefficient frontier and providing an overly optimistic assessment. Hence, there is a growing need for methods that consider both frontiers to overcome this issue.

In this study, we introduce an enhanced DFM model that integrates both optimistic and pessimistic distance analyses. The research methodology is as follows: IDMU-based CCR and ADMU-based CCR models are designed and implemented to calculate the optimistic and pessimistic efficiency of DMUs, respectively. Then, additive models based on virtual IDMU and ADMU units are designed and implemented. Subsequently, DMUs in both approaches are categorized, and DMUs of the third category of each approach are entered into the respective DFM model. After calculating the distance of each DMU from both efficient and inefficient frontiers, the relative closeness (RC) index is employed to aggregate the distances of DMUs from the efficient and inefficient frontiers. Finally, the DMUs are ranked based on the RC index. To demonstrate the practicality of the model, we evaluate the sustainable performance of OECD countries concerning CO2 emissions. Our findings illustrate that the model can measure DMUs' distances to both efficient and inefficient frontiers, providing policymakers dealing with Sustainable Development Goals (SDGs) a more nuanced understanding of the situation.

In summary, the DFM model proposed in this study bridges the gap by considering optimistic and pessimistic perspectives, offering a more comprehensive view of DMU performance.

Data envelopment analysis (DEA) is a mathematical programming method that can evaluate the relative efficiency of multiple inputs and multiple outputs of a decision-making unit (DMU). The classical DEA model assumes that inputs and outputs are determined. However, there are some applications where the inputs–outputs are stochastic. In practice, it is important to evaluate stage performance. It is essential to eliminate the effect of preceding stage inputs (outputs) on stage performance in order to accurately assess stage performance. In this paper, we propose stage stochastic incremental DEA models that integrate two different kinds of inputs and outputs. The first kind of model takes into account the assessment of stage efficiency when determinate incremental inputs and stochastic incremental outputs are applied at the beginning and end of the stage. The second kind of model uses stochastic incremental inputs–outputs to evaluate stage efficiency. To verify the efficacy of the suggested models, the first kind of model is applied to assess the stage financing efficiency of 15 energy-saving and environmental protection clean enterprises (ESEPCEs). The second kind of model is applied in assessing the stage investment efficiency of 15 ESEPCEs. The empirical results show that the proposed models not only eliminate the effect of prior performance but also more accurately assess stage efficiency in a stochastic environment.

The air in the Lombardy Plain, Italy, is one of the most polluted in Europe due to limited atmosphere circulation and high emission levels. There is broad scientific consensus that ammonia (NH${}_3$) emissions have a primary impact on air quality, and in Lombardy, the agricultural sector and livestock activities are widely recognised as being responsible for approximately 97% of regional ammonia emissions due to the high density of livestock.

In this paper, we quantify the relationship between ammonia emissions and PM_2.5 concentrations in the Lombardy Plain and evaluate PM_2.5 changes due to the reduction of ammonia emissions through a ‘what-if’ scenario analysis. The information in the data is exploited using a spatiotemporal statistical model capable of handling spatial and temporal correlation as well as missing data. To do this, we propose a new heteroskedastic extension of the well-established Hidden Dynamic Geostatistical Model. Maximum likelihood parameter estimates are obtained by the expectation–maximisation algorithm and implemented in a new version of the D-STEM software.

Considering the years between 2016 and 2020, the scenario analysis is carried out on high-resolution PM_2.5 maps of the Lombardy Plain. As a result, it is shown that a 26% reduction in NH${}_3$ emissions in the wintertime could reduce the PM_2.5 average by 1.44 $\mu g/m^3$ while a 50% reduction could reduce the PM_2.5 average by 2.76 $\mu g/m^3$ which corresponds to a reduction close to 3.6% and 7% respectively. Finally, results are detailed by province and land type.