Integrated Evaluation Framework Research Articles

Environmental risk evaluation for radionuclide transport through natural barriers of nuclear waste disposal with multi-scale streamline approaches

Natural barriers, encompassing stable geological formations that serve as the final bastion against radionuclide transport, are paramount in mitigating the long-term contamination risks associated with the nuclear waste disposal. Therefore, it is important to simulate and predict the processes and spatial-temporal distributions of radionuclide transport within these barriers. However, accurately predicting radionuclide transport on the field scale is challenging due to uncertainties associated with parameter scaling. This study develops an integrated evaluation framework that combines upscaled parameters, streamline transport models, and response surface techniques to systematically assess environmental risk metrics and parameter uncertainties across different scales. Initially, upscaling methods are established to estimate the prior interval of critical transport parameters at the field scale, and streamline models are derived by considering the radionuclides transport with a variety of physicochemical mechanisms and geological characterizations in natural barriers. To assess uncertainty ranges of the risk metrics related to upscaled parameters, uncertainty quantification is performed on the ground of 5000 Monte Carlo simulations. The results indicate that the upscaled dispersivity of fractured media (αLf) has a relatively high sensitivity ranking on release dose for all nuclides, and upscaled matrix sorption coefficient (Kd) of Pu-242 strongly affects breakthrough time and release dose of Pu-242. Facilitated by robust response surface with the lowest R2 of 0.89, it is shown that the release doses of Pu-242 and Pb-210 increase under conditions of low Kd and αLf, respectively. Furthermore, statistical analysis reveals that employing limited laboratory-scale parameters results in narrower confidence intervals for risk metrics, while upscaling methods better account for the highly heterogeneous properties of large-scale field conditions. The developed risk evaluation framework provides valuable insights for utilizing upscaled parameters and modeling radionuclide transport within natural barriers under various scenarios.

An integrated evaluation framework for Environmental, Social, and Governance-driven social media performance through Multi-criteria Decision-Analysis

Social media is crucial in providing data for networking with individuals, companies, or industries. This study deals with the topic of Environmental, Social and Governance (ESG). These can be seen as a set of standards which impacts a corporation’s structure and performance. Twenty years ago, when ESG started to gain notoriety, ESG initiatives were labeled as a tool to ensure ‘ethical’ and/or ‘responsible’ investing. This study collects data from LinkedIn company profiles on the industry, the staff, the number of followers, and the state where the company has its headquarters. In addition, the media’s take on pending legislation on ESG was assessed. Based on data from 537 companies, indicators were created, either normalized, the median, or ranked, and analyzed with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The current work provides information on this variation of ESG activity within the different states in the USA. Multi-criteria Decision Analysis (MCDA) was applied by weighing four criteria. The overall assessment was presented depending on the combined weighted performance score of the four criteria. The results showed that 36% of the companies active within the ESG landscape represent the “Financial Services” and “Management Consulting” industries. 65% of the investigated sample represented companies with up to 10 employees. Moreover, eight states perform well on both median of staff and followers (in alphabetical order: Michigan, Mississippi, Missouri, New Hampshire, New Mexico, Ohio, Oklahoma, and Wisconsin). Lastly, through the MCDA method, it was also observed that there are not many variations when different weights are applied to the four criteria: i.e., “the normalized number of companies by state,” “the median of the followers of the company per state,” “the median of the staff of the company per state,” and the “sentiment of the legislation.” Finally, the top five highly ranked states considering ESG visibility on LinkedIn through their companies are Ohio, Michigan, Delaware, Georgia, and Pennsylvania. The results indicate that companies can more proactively advertise themselves as compliant with current ESG principles. This methodology can also be applied to other topics and regions to assess online activity in line with existing indicators.

An evaluation framework for low performers and human resource management planning: Application to a life insurance company in Korea

This study comprehensively explores the factors that lead to low performers in an organization. A thorough literature review was conducted to construct an interview guide and obtain classification criteria for the factors that lead to low performers. Managers and low performers at multiple firms were interviewed individually to understand the various phenomena related to low performers in organizations. Based on the content of these interviews, 12 factors, classified into individual, leader, work, and organizational dimensions, were identified after three rounds of revisions by business administration experts. Next, a case study of Korea’s S Life Insurance Company was conducted to examine the practical implications of the factors that contribute to creating low performers. In this case study, the analytic hierarchy process (AHP), involving eight departmental heads S Life Insurance Company’s HR division, was utilized to identify the main factors that must be considered when evaluating low performers. While previous studies have examined low performers either at the individual, organizational, or institutional levels, this study presents a comprehensive and integrated evaluation framework of the factors that cause low performers. The proposed framework facilitates the identification and evaluation of low performers in various organizations and industries, and thus has practical implications in terms of establishing strategies to manage low performers more efficiently and improve organizational performance.

Introducing an integrative evaluation framework for assessing the sustainability of different types of urban agriculture

ABSTRACT In cities, a mosaic of different types of urban agriculture can be found. However, knowledge about advantages and disadvantages of the different types is still fragmented. This paper introduces an integrative evaluation framework for assessing the environmental, social, and economic sustainability of urban agriculture by applying a multi-criteria analysis based on an Analytic Hierarchy Process and a participatory approach. Based on a German case study and on the examples of vertical farming and community-supported agriculture, the results suggest that sustainable urban agriculture is a multi-dimensional approach informed by strong sustainability that places nature in the focus. Thus, the environmental dimension received the highest weight, followed by the social and, lastly, the economic dimension. Regarding the sub-criteria, species diversity achieved the highest total weight and food quality and safety the lowest. Conceptually, this paper provides scientific fundamentals for a systematic comparative evaluation of different types of agriculture for sustainable urban development.

Machine learning enhanced evaluation of semiconductor quantum dots

A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.

Expert Review on Usefulness of an Integrated Checklist-based Mobile Usability Evaluation Framework

Previous mobile usability studies are only pertinent in the context of ergonomics, physical user interface, and mobility aspects. In addition, much of the previous mobile usability conception was built on desktop computing measurements, such as desktop and web application checklists, or scarcely addressed the mobile user interface. Moreover, the studies focus mainly on interface features for desktop applications and do not reflect comprehensive mobile interface features such as navigation drawers and spinners. Therefore, conducting usability evaluation using conventional usability measurement would result in irrelevant results. In addition, the resulting works are tailored for usability testing, which requires highly skilled evaluators and usability specialists (e.g., usability testers and user experience designers), who are rarely integrated into a development team. The lack of expertise could lead to unreliable usability evaluations. This paper presents a review from industrial experts on a comprehensive and feasible usability evaluation framework developed in our previous work. The framework is dedicated to smartphone apps, which integrate evaluator skills and design concerns. However, there is no evidence of its usefulness in practice. Therefore, the usefulness of the framework measurement for evaluating apps' usability in the eyes of non-usability specialists is empirically assessed in this paper through an expert review. The expert review involved eleven industrial developers and was complemented by a semi-structured interview. The method is replicated in comparison with a framework from another study. The findings show that the formulated framework significantly outperformed the framework (p = 0.0286) from other studies with large effect sizes (r = 1.81) in terms of usefulness.

Uncovering the trade-induced air pollution inequality among Belt and Road Initiative (BRI) countries

Although trade does not only boost the economy but also exacerbates environmental transfers but still the evidence for air pollution's unequal exchange embodied in trade among the Belt and Road Initiative (BRI) countries is rarely discussed in the extant literature. From the perspective of an integrated evaluation framework comprising a multi-regional input-output model, system balance theory, and complex network theory, this research paper uncovers the spatial- and temporal changes of trade-induced air pollution inequality among countries along the BRI route for the time period from 2011 to 2016. The study findings point out that there exists an obvious spatial heterogeneity in the unequal relations among BRI economies. This implies that a large number of country pairs demonstrate weak inequalities, whereas a relatively small number of country pairs represent strong inequalities. In addition, the countries' positions indicate a unique characteristic of “duality and inverse”, which is majorly manifested as pure causers represented by Moldova and Belarus, net beneficiaries symbolized by China and Russia, and pure victims exemplified by Bhutan. Meanwhile, nearly half of the dual identities are represented by Singapore. Besides this, the proposal and implementation of the BRI exert a positive influence on both system stability and inequality mitigation by supporting the balance of triangles. Finally, this paper posits that the environmental inequality system of the Belt and Road is not as complex as imagined since the 3-node motif classification is relatively simple. Our findings are helpful for mitigating air pollution inequalities so as to support the sustainable development of green BRI.

Challenges to implementing a sustainable strategic evaluation framework of industrial parks: Mexican case

Industrial parks are essential in driving regional development, but how and how much do they benefit their regions? These questions motivated us to develop and implement a framework to evaluate the outputs of industrial parks (I.P.). Previous research developed the integrative framework Sustainable Strategic Evaluation Framework (SSEF) based on Managerial and Contextual Governance components. Relevant indicators for sustainability make part of the framework and can be adapted to match the availability of specific/local data. In this article, we report on implementing the Contextual Governance component of the SSEF to explain the dependency influence of contextual governance characteristics upon sustainability indicators for the regional impact of I.P. By analyzing the challenges during the implementation phase of the qualitative research, we bring new knowledge that can contribute to further developing sustainable strategic evaluation frameworks of I.P. We selected the case study approach to implement the SSEF, which includes an in-depth analysis. The industrial park Guanajuato Puerto Interior (GPI) in Mexico was chosen as sustainability principles have been embedded in this I.P. since its creation. The findings of this first SSEF implementation show that the Contextual Governance component was evaluated as RED restrictive in the criteria of Flexibility and Intensity of two governance dimensions: (i) Levels & scales and; (ii) Problem perspectives & goal ambitions. The other criteria were evaluated as YELLOW moderative, and only in the criteria of Extent related to the governance dimensions Levels& scales and Strategies and instruments was a GREEN enabling evaluation. The challenges identified during the SSEF implementation were directly associated with having a clear definition of the contextual governance factors and processes that simultaneously allow the identification of data to monitor the sustainability performance of the I.P.

Impacts of the clean residential combustion policies on environment and health in the Beijing–Tianjin–Hebei area

The Chinese government has implemented a series of air pollution control measures in the residential combustion sector, especially in the Beijing–Tianjin–Hebei (BTH) area. The government launched the Three-year Action Plan for Winning the Blue Sky War in 2018. Cutting down the coal combustions in the residential sector has been highly addressed. To compare the environmental and health impacts, we set two scenarios: implementing the residential coal-switch policy and excluding the policy, through an integrated evaluation framework consisting of the GAINS IV Asia model and the IMED/HEL model. Results show that compared with the 2030 Baseline, emission of NOx, SO2, and PM2.5 in BTH would decline by 49.7 kt (3.8%), 216.2 kt (20.6%), and 199 kt (25.7%), respectively. Meanwhile, it leads to reductions of PM2.5 concentrations by 9.8 μg/m³ (19.9%), 3.1 μg/m³ (6.4%), 5.0 μg/m³ (10.7%) in Beijing, Tianjin, and Hebei, respectively. However, PM2.5 concentration in BTH would still exceed the World Health Organization Interim Target-1 of 35 μg/m3 by 2030. Carrying out the residential coal-switch policy can avoid 0.78 million morbidities (15.2%) and 0.01 million mortalities (5.6%) in BTH by 2030 compared to Baseline. Further, work time loss would decrease by 15.1% in 2030. The value of statistical life (VSL) of 9.58 billion USD (12.4%), 2.23 billion USD (3.8%), and 12.48 billion USD (6.6%) would be saved for Beijing, Tianjin, and Hebei by 2030. Regions with heavy pollution have benefited more from the clean residential combustion strategies and Hebei would account for 50.8%–62.3% of the total avoided health benefits in 2030. The benefit-cost ratio in Beijing is much higher than that in other regions. Implementing the residential coal-switch policies poses remarkable co-benefits on human health and the economy. This research lays a foundation for future environmental and health risks in other Chinese cities.

Towards an integrated framework for evaluating transformative innovation policy

In recent years, a new frame for innovation policy has emerged, namely “transformative innovation policy” (TIP), which aims at addressing transformative change or “Grand Challenges”. Such a shift in policy theory should, ideally, be reflected in policy evaluation, but the literature has so far provided little advice on how to address TIP-related evaluation challenges such as directionality and system-level behavioural additionality. This paper discusses how the evaluation of policy interventions targeting system innovation can be designed to address these challenges. Combining the literature on sustainability transitions with policy evaluation, we propose an integrated evaluation framework composed of three main components: (i) programme theory (programme goals, systems boundaries and desired (or accepted) development paths); (ii) system analysis (transformative outcomes); and (iii) synthesis and overall assessment (including revision of programme theory). By integrating the two sets of literature, we provide a bridge between academic research on transitions and current evaluation practices. We briefly illustrate the applicability of the integrated framework in the BioInnovation Strategic Innovation Programme in Sweden.

Applicable Framework for Evaluating Urban Vitality with Multiple-Source Data: Empirical Research of the Pearl River Delta Urban Agglomeration Using BPNN

Urban vitality is a mirror reflection of ‘urban disease’ in cities. The research on urban vitality has made great progress in evaluation frameworks; however, these frameworks cannot jointly account for the macro and micro performance of urban vitality. It is better to establish an integrated evaluation framework for this topic. This paper defines urban vitality as the comprehensive strength to support dense and diverse activities based on urban development and the urban environment, and subsequently develops an integrated framework including economic, social, cultural, and spatial dimensions. With the nonlinear evaluation model of a back propagation neural network, we further presented the result of an application on the Pearl River Delta urban agglomeration. Our profiling results illustrate the core-edge structure of urban vitality. There are differences in vitality performance within built-up areas, which shows that areas with urban landscapes and excellent infrastructure are more vibrant. The integrated framework with good applicability improves the evaluation of urban vitality that is crucial to city examination and urban planning. Hence, this study provides a comprehensive reference for optimizing resource allocation and promoting sustainable development.

Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine

Land surface albedo plays an important role in controlling the surface energy budget and regulating the biophysical processes of natural dynamics and anthropogenic activities. Satellite remote sensing is the only practical approach to estimate surface albedo at regional and global scales. It nevertheless remains challenging for current satellites to capture fine-scale albedo variations due to their coarse spatial resolutions from tens to hundreds of meters. The emerging Sentinel-2 satellites, with a high spatial resolution of 10 m and an approximate 5-day revisiting cycle, provide a promising solution to address these observational limitations, yet their potentials remain underexplored. In this study, we integrated the Sentinel-2 observations with an updated direct estimation approach to improve the estimation and monitoring of fine-scale surface albedo. To enable the capability of the direct estimation approach at a 10-m scale, we combined the 10-m resolution European Space Agency (ESA) WorldCover land cover data and the 500-m resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/albedo product to build a high-quality and representative BRDF training database. To evaluate our approach, we proposed an integrated evaluation framework leveraging 3-D physical model simulations, ground measurements, and satellite observations. Specifically, we first simulated a comprehensive dataset of Sentinel-2-like surface reflectance and broadband albedo across a variety of geometric configurations using the MODIS BRDF training samples. With this dataset, we built the Look-Up-Tables (LUTs) that connect surface broadband albedo and Sentinel-2 reflectance through a direct angular bin-based linear regression approach, and further coupled these LUTs with the Google Earth Engine (GEE) cloud-computing platform. We next evaluated the proposed algorithm at two spatial levels: (1) 10-m scale for absolute accuracy assessment using the references from the Discrete Anisotropic Radiative Transfer (DART) simulations and flux-site observations, and (2) 500-m scale for large-scale mapping assessment by comparing the estimated albedo with the MODIS albedo product. Lastly, we presented four examples to show the capability of Sentinel-2 albedo in detecting fine-scale characteristics of vegetation and urban covers. Results show that: (1) the proposed algorithm accurately estimates surface albedo from Sentinel-2-like reflectance across different landscape configurations (overall root-mean-square-error (RMSE) = 0.018, bias = 0.005, and coefficient of determination (R2) = 0.88); (2) the Sentinel-2-derived surface albedo agrees well with ground measurements (overall RMSE = 0.030, bias = -0.004, and R2 = 0.94) and MODIS products (overall RMSE = 0.030, bias = 0.021, and R2 = 0.97); and (3) Sentinel-2-derived albedo accurately captures seasonal leaf phenology and rapid snow events, and detects the interspecific (or interclass) variations of tree species and colored urban rooftops. These results demonstrate the capability of the proposed approach to map high-resolution surface albedo from Sentinel-2 satellites over large spatial and temporal contexts, suggesting the potential of using such fine-scale datasets to improve our understanding of albedo-related biophysical processes in the coupled human-environment system.

Spatial supply-demand balance of green space in the context of urban waterlogging hazards and population agglomeration

Urban green space (UGS) contributes significantly to urban settlements with respect to frequent rainstorm events and population agglomeration issues, but there is limited understanding of the spatial match between green space supply and social demand. This study established an integrated evaluation framework relating to the supply–demand balance of UGS. In a case study of Beijing, the urban waterlogging risk was simulated with an ICM model, and specific demand criteria of waterlogging hazards and population needs were considered. The street-scale supply–demand balance of demands for existing and future UGS was then quantified with the two-step floating catchment area method. Comparison of the street-level balance in large rainstorms and populated areas identified the key locations where the green space supply should be strengthened in Beijing. This study may provide an integrated evaluation of green space accessibility and a solid foundation for refined urban land-use planning strategies.

Scenario building model to support the resilience planning of winemaking regions: The case of the Douro territory (Portugal).

Today resilience paradigm is shared by both academic and political debates, and it is gradually being implemented into plans, programmes, and project's initiatives. In this context, wine regions, particularly those designated as UNESCO sites, show their robustness and vulnerabilities as Socio-Ecological Systems. The climate change impact is increasing the exposure of their aesthetic, cultural, economic, natural values to potential losses and therefore an effective response is required To build resilient strategies, Decision Makers are increasingly considering multidimensional models in planning and assessment processes. This paper continues a research work that develops an integrated evaluation framework constituted by Multicriteria Decision Analysis to calculate a Territorial Resilience Index, a dynamical model to predict future ecological scenarios and scenario building to define a resilient strategy for the Douro territory (Portugal). This paper focuses on scenario building which uses specific tools such as Social Network Analysis to identify and investigate the relationships between real actors and stakeholders, Wilson matrix to assess the degree of uncertainty and relevance of the strategies identified by a panel of specialists, and consistency matrix to evaluate the strategies consistency in achieving the Sustainable Development Goals and UNESCO requirements. The obtained protocol of guidelines and recommendations can support Decision Makers in the design of sustainable and resilient transformation, thereby assisting in the preservation of the UNESCO site.

Regional years of life lost, years lived with disability, and disability-adjusted life-years for severe mental disorders in Guangdong Province, China: a real-world longitudinal study

BackgroundTo understand the magnitude and spatial–temporal distribution of the regional burden attributable to severe mental disorders is of great essential and high policy relevance. The study aimed to address the burden of severe mental disorders by evaluating the years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) in Guangdong, China.MethodsWe undertook a longitudinal study based on a multicenter database established by the Health Commission of Guangdong, involving a total of 21 prefectures and four economic regions in the Guangdong province. A total of 520,731 medical records from patients with severe mental disorders were collected for 2010–2020. Data were analyzed via an integrated evaluation framework by synthesizing prevalence estimates, epidemiological adjustment as well as comorbidity assessment to develop internally consistent estimates of DALY. DALY changes during 2010–2020 were decomposed by population growth and aging and further grouped by Socio-demographic Index (SDI). DALYs were projected to 2030 by the weighted median annualized rate of change in 2010–2020.ResultsIn 2010–2020, the average DALYs for severe mental disorders reached 798,474 (95% uncertainty interval [UI]: 536,280–1,270,465) person-years (52.2% for males, and 47.8% for females). Severe mental disorders led to a great amount of disease burden, especially in Guangzhou, Shenzhen, and Foshan cities. Schizophrenia and mental retardation with mental disorders were the two leading sources of the burden ascribed to severe mental disorders. Population growth and aging could be accountable for the increasing burden of severe mental disorders. Economic regions with higher SDI carried a greater burden but had lower annualized rates of change in DALYs. The overall burden of severe mental disorders is projected to rise modestly over the next decade.ConclusionsThe findings urge prioritization of initiatives focused on public mental health, prevention strategies, health resources reallocation, and active involvement of authorities to effectively address the anticipated needs.

Dynamic collaborative optimization for disaster relief supply chains under information ambiguity.

Large-scale disasters occur worldwide, with a continuing surge in the frequency and severity of disruptive events. Researchers have developed several optimization models to address the critical challenges of disaster relief supply chains (e.g., emergency material reserving and scheduling inefficiencies). However, most developed algorithms are proven to have low fault tolerance, which makes it difficult for disaster relief supply chain managers to obtain optimal solutions and meet the emergency distribution requirements within a limited time frame. Considering the uncertainty and ambiguity of disaster relief information and using Interval Type-2 Fuzzy Set (IT2TFS), this paper presents a collaborative optimization model based on an integrative emergency material supplier evaluation framework. The optimal emergency material suppliers are first selected using a multi-attribute group decision-making ranking method. Multi-objective fuzzy optimization is then run in three emergency phases: early -, mid-, and late-disaster relief stages. Focusing on a massive flash flood disaster event in Yunnan Province as a case study, a comprehensive numerical analysis tests and validates the developed model. The results revealed that the proposed optimization method can optimize emergency material planning while ensuring that reserve material safety inventory is always maintained at a reasonable level. The presented method suggests a fuzzy interval to prevent emergency materials’ safety inventory shortage and minimize continuous life/property losses in disaster-affected areas.

An integrated evaluation framework for multiscale ecological protection and restoration based on multi-scenario trade-offs of ecosystem services: Case study of Nanjing City, China

Ecological protection and restoration is an important bridge to adjust the contradiction between ecological protection and economic development, and it is essential for ecological security and sustainable development. An integrated evaluation framework for multiscale ecological protection and restoration based on multi-scenario trade-offs of ecosystem services was proposed in this study, and the city of Nanjing was taken as an example to identify key areas and land strategies for ecological protection and restoration. In our framework, carbon storage, habitat quality, nitrogen retention, and water conservation services were quantified by InVEST model, and recreational service was quantified as the total areas of green spaces within ecological land accessibility. The ordered weighted averaging model was used to simulate the priority areas of ecological protection under 11 scenarios of trade-offs of ecosystem services, and ecological sources, ecological corridors, ecological pinch points and ecological barriers at the Nanjing city scale and the main urban scale were identified based on circuit theory. We also nested the ecological conservation and restoration patterns at two scales and analyzed the coincidence degree between two scales. Our results showed that the S7 scenario had the highest average conservation efficiency among the 11 scenarios. The priority areas for ecological protection in this scenario had an area of 1582 km2 and are mainly distributed in the central and southern parts of Nanjing. In our study, 50 ecological sources (with an area of 1153.36 km2), 77 ecological corridors (with a length of 443.04 km), 25 key nodes for ecological protection and 31 key nodes for ecological restoration were identified in Nanjing. By nesting the ecological protection and restoration patterns of two scales, we found that the overlap of ecological sources and ecological pinch points at two scales is 100%, and the overlap of ecological corridors and ecological barriers is 45.28% and 57.14%, respectively, which means that the ecological protection and restoration patterns at the two scales in Nanjing were well connected, but the ecological network at the large scale cannot fully reflect the real ecological problems and needs at the small scale. We suggest regarding the ecological protection and restoration pattern at large scale as the basis and the pattern at small scale as the supplement. We also propose that priority should be given to the protection and restoration of key areas where the two scales overlap, and strictly control their land development to promote sustainable development. This framework and these strategies are applicable to other rapidly urbanizing regions of the world.

Integrated emergy and economic evaluation of 8 hydropower plants in Zagunao Basin, Southwest of China

Hydropower has been regarded as a clean energy to replace fossil fuels facing the climate change, while detecting the tradeoffs among renewable energy production, greenhouse gas (GHG) mitigation and ecological impacts still remains challenge. Multi-disciplinary analysis should be adopted to understand these tradeoffs. This study proposed an integrated evaluation framework based on donor-side (emergy analysis) and user-side (economic analysis) which highlighted the impacts on ecosystem services. 8 hydropower plants in Zagunao Basin, Southwest China, were adopted as the study case. The results show that impacts on water supply and biodiversity dominated ecosystem service losses. Plants with higher water head are more efficient in resource utilization, while the efficiency of plants with gravity dams are generally lower than that with grille weirs under the similar installed capacity. Lixian plant had the best performance based on emergy indicators attributed by the benefits to utilize the renewable source from the regulating flow ability of Shiziping plant. Results from fair prices reflect that all the studied plants are not profitable under current grid price. Finally, policy implications were raised based on the results.

Exploring teaching effectiveness and research on teaching and learning at AACSB accredited business schools in Canada and the US

PurposeThis study explores the implementation of two Association to Advance Collegiate Schools of Business (AACSB) standards by business schools across Canada and the US. First, this study examines how teaching effectiveness is defined and measured in light of Standard 7 (Teaching Effectiveness and Impact). Second, this study explores the value of research on teaching and learning in relation to Standard 8 (Impact of Scholarship).Design/methodology/approachThis study adopts a thematic analysis framework based on data obtained from an online survey, semi-structured interviews, and policy documents.FindingsThe results reveal that business schools rarely define teaching effectiveness; instead, they adopt various measures to evaluate teaching effectiveness. The results reveal that research on teaching and learning alone usually does not lead to tenure; however, it is valued if part of a portfolio that includes discipline-specific research. Lastly, this research highlights a stigma associated with research on teaching and learning relative to discipline-specific research.Practical implicationsThis study introduces a comprehensive and integrated teaching evaluation framework that can be adopted to define teaching effectiveness and elevate the teaching function. In addition, the authors argue that business schools should nurture a niche set of academics that holds PhDs in their respective disciplines and are education experts to increase the production of research-informed instructional strategies curated for business schools.Originality/valueThis is the first study to explore how AACSB standards related to teaching effectiveness and research on teaching and learning are interpreted and implemented at AACSB accredited business schools.

Carbon footprint, yield and economic performance assessment of different mulching strategies in a semi-arid spring maize system

Crop productivity maximization while minimizing carbon emissions is of critical importance for achieving sustainable agriculture. Socio-economic and ecological benefits should be taken together under the circumstance of stagnant farming profitability and climatic variability. The effectiveness of various mulching strategies in rain-fed semiarid areas has been confirmed, but scarce the comprehensive evaluations of the conventional and new mulching strategies in terms of yield, economic benefit, and carbon footprint based on life cycle assessment (LCA) have been conducted. Hence, a two-year field experiment was conducted on maize (Zea mays L.) crop to explore the effects of four mulching strategies (PM: plastic-film mulching, SM: maize straw mulching, BM: biodegradable-film mulching, and NM: no mulching) on the yield, net return, greenhouse gas (GHG) emissions, and carbon footprint (CF). The results revealed that PM and BM significantly increased maize yield by 11.3–13.3% and 9.4–10.6%. PM marginally raised the net return by 2.0–2.4% whereas BM slightly reduced it by 4.6–8.8% relative to NM. Unexpectedly, the yield and net return were the lowest under SM, and intensified N2O emissions, GWPdirect, and yield-scaled GWPdirect were observed. When the GHGs using LCA concept and SOC sequestration rate were considered, the lowest net GWP (1804.1–1836.4 kg CO2-eq ha−1) and CF (148.9–119.9kg CO2-eq t−1) were observed in the SM treatment due to the boost of soil organic carbon (SOC) sequestration. Conversely, PM and BM significantly increased the net GWP and CF compared to NM. When the tradeoffs between the high production, high net return and low net GWP were assessed by an integrated evaluation framework, the NM was recommended as an efficient low-carbon agricultural practice in the rain-fed semiarid areas.