Multi-indicator Assessment Research Articles

Navigating urban human settlement sustainability: A multi-indicator assessment based on sustainable development goal 11

The sustainability of cities determines human sustainable development. Human settlement is an important element of cities, and scientific monitoring and assessment of it can help cities accurately identify unsustainable issues. Taking Sustainable Development Goal 11 as the basic framework, the study constructs a global development goal-oriented sustainability assessment system for urban human settlement (UHS), including 7 fields and 26 indicators, which serves as a reference for cities globally. And an empirical analysis is conducted using panel data of 143 typical cities in China from 2015 to 2019. The results show that: (1) the score of China's UHS sustainability improved from 54.84 to 58.52, with some cities already achieving the goal of sustainable UHS. More than 50% of the cities were on track to achieve the goal by 2030. (2) Regional scores varied, being higher in the south and lower in the north, with the highest score in the eastern coastal region and the lowest score in the northeastern region. (3) Economy size is significantly and positively correlated with UHS sustainability, which is consistent with the regional performance, but with a marginal decreasing trend. (4) The performance of cities with different population size varied more significantly. The scores of UHS sustainability showed a U-shaped relationship with population size. In response to these findings, this study proposes corresponding strategic measures. The insights gained from the empirical analysis of Chinese cities can inform global policymaking.

A global multi-indicator assessment of the environmental impact of livestock products

Driven by a growing and more affluent population, changing diets and lifestyles, the demand for livestock products is expected to surge in the next decades. Satisfying this demand will result in additional pressures on land systems. The increasingly globalized supply chains of the livestock economy will further decouple many of these impacts from the places where livestock are reared. In this study, we determined the impact intensities of global livestock production across three environmental indicators: deforestation, biodiversity loss and marine eutrophication. To this end, we used global data on the production of crops (and grass), their trade and use as feed in livestock-production systems, as well as livestock production data. We found the highest deforestation and biodiversity impact intensities in the tropics in Central and South America, Southeast Asia and Central Western Africa. In contrast, the highest values for marine eutrophication intensities were found in countries located in Northern Europe and in South and in East Asia. Our analyses show differences caused by varying efficiencies in livestock production systems and by the sourcing patterns of feed items. In grazing systems for the production of ruminant meat, for example, the resulting impact intensities are dominated by the consumption of grass. In intensive and industrialized production systems, the bulk of the deforestation and, to a lesser extent, biodiversity impacts are linked to imported soybean feed. Our results can help identify livestock production systems and countries that would qualify as priority action targets, as well as potential entry points to make their livestock production systems more sustainable. They can also be used to assist consumers in comparing impacts across and within livestock food product types. Ultimately, understanding the environmental impacts embodied in global supply chains of livestock products can help create better regulatory policies and science-based interventions for protecting terrestrial and marine ecosystems.

Multi-Indicator Assessment of a Thermal Insulation Investment, Taking into Account the Pre-Set Temperature

The article proposes indicators to evaluate a thermal insulation investment in a building, such as net present value (NPV), profitability index, discounted payback period, and ecological cost efficiency. Economic and ecological aspects were taken into account. Life Cycle Assessment (LCA) was used in the ecological analysis. The following heat sources in the building were considered: condensing gas boiler and heat pump. The developed indicators also depend on the pre-set temperature in residential premises. A methodology to determine the optimum thermal insulation thickness for both economic and ecological reasons was also proposed. A case study was analyzed, and a reference building, typical for Polish construction conditions, was used for research. Various solutions were suggested regarding the type of thermal insulation material and heat sources. The values of the indicators were determined for the proposed variants and for the economically and ecologically optimum thermal insulation thicknesses. Based on the conducted research, it was found that air temperatures maintained in the rooms of the building undergoing thermal modernization should be taken into account in the energy audit. The energy demand of the building for a room temperature of 26 °C is higher by 61% compared to the demand for the same building at the design temperature (20 °C). The innovation in the proposed approach to the economic and ecological assessment of a building is the combination of a wide range of temperatures potentially maintained in living spaces with ecological cost-effectiveness.

Growth Performance and Environmental Quality Indices and Biomarkers in a Co-Culture of the European Sea Bass with Filter and Deposit Feeders: A Case Study of an IMTA System

This study aimed to evaluate the efficiency of an integrated multi-trophic aquaculture (IMTA) system comprising co-cultured fed fish and organic extractive species representing three distinct trophic levels as well as the impact and potential utilization of two commercially available fish feeds made up of 35% fish meal (FM) and 20% fish meal (LFM) ingredients, using a multi-indicator assessment approach. Significant alterations were observed in growth performance indicators (GPIs), water and sediment quality indices, toxicity tests and biomarkers within the IMTA system. The fish survival, weight gain (WG), and specific growth rate (SGR) were higher in the IMTA system with significantly lower feed conversion ratios (FCRs) and higher feed efficiency (FE) in comparison to the fed fish monoculture system. Yet, organic filter feeders displayed 100% survival, and increased shell growth, while deposit feeders exhibited successful survival and significant weight gain. In the comparison between FM-IMTA and LFM-IMTA, fed fish in FM-IMTA showed higher WG, SGR, and FE with lower FCR. Environmental parameters like temperature, oxygen, and nutrient concentrations fluctuated but generally improved in the IMTA system, indicating lower mesotrophic conditions. Sediment fatty acid profiles differed between systems and toxicity assessments, which suggested a lower impact in IMTA and FM-IMTA systems. The sediment microbial community displayed high similarity within IMTA systems and between FM-IMTA and LFM-IMTA. These findings underscore the potential of IMTA systems for sustainable aquaculture, emphasizing improved growth performance and reduced environmental impact, particularly when using fish meal feeds.

Development of an industrial solid waste ecological analysis model in Shanghai, China.

Amid China's rapid economic expansion, the country's industrial solid waste (ISW) problem is escalating. As each sector generates distinct types of ISW, a multi-indicator assessment of each sector is essential to address China's New Solid Waste Policy. To investigate the ISW situation of each sector and perform a comprehensive assessment, we formulate an industrial solid waste ecological analysis framework based on ISW generation and ISW flow in the sector. Various indicators (i.e., solid waste utilization coefficient, solid waste threat coefficient, and solid waste threat intensity) are employed to assess the utilization of solid waste generated for each sector, as well as the threat of solid waste originating in each sector to society. Ecological network analysis probes the interrelationships between diverse sectors. Taking Shanghai in 2017 as an example, the study indicates that some sectors (e.g., production and supply of electric power and heat power (EH) and metal smelting and rolling processing sector (MS)) exhibit higher direct ISW generation and the direct industrial solid waste value-added coefficient (SVAC) for common industrial solid waste (CISW). Specifically, the direct CISW generation of EH and MS is 539.21Mt and 277.00Mt respectively. The direct SVAC of EH and MS is 157.06kg/103RMB and 126.27kg/103RMB respectively. These sectors should prioritize reducing emissions at the source. Additionally, the threats to society from various sectors are relatively insignificant for the CISW, while for the hazardous waste (HW), all sectors pose a considerable threat to Shanghai's society. Moreover, some sectors (e.g., mining industry) exhibit the highest mutualism relationships in the CISW and the HW. Enhancing mining sector technologies is a vital strategy for mitigating ISW sources. Specifically, MI has 9 pairs of mutualism relationships in the CISW and 8 pairs in the HW. These insights will provide empirical evidence for tackling the ISW problem in Shanghai.

Economic loss assessment of typhoon-induced storm surge disasters in the South China Sea based on GSA-BP model

In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989–2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea.

Optimize farm size and agronomic practices to improve agricultural sustainability: a case of multi-indicator assessment from the North China Plain

Optimize farm size and agronomic practices to improve agricultural sustainability: a case of multi-indicator assessment from the North China Plain

A global analysis of potential self-sufficiency and diversity displays diverse supply risks

International trade plays a fundamental role in today's globalized food system, however, trade-related disruptions to national food supply have become increasingly prevalent. Although national food self-sufficiency and the resilience of domestic food production are both increasingly discussed, they are rarely investigated in tandem. This hinders our understanding of the diversity of risks to national food supply. In this article we investigate the contribution of production to these risks, through the compilation of a comprehensive national production dataset and a multi-indicator assessment of self-sufficiency and diversity. Our results show that most of the world (127 countries and territories, 87% of the global population) achieves high levels of potential self-sufficiency (≥6 nutrients fulfilled), however only 33% of the world population (41 countries) are fully self-sufficient. Of countries with high levels of self-sufficiency, fruit and vegetable production (a proxy for many micronutrients) is the most common “missing” sufficiency. 66 countries (6% of population) have a low degree of self-sufficiency, highlighting potential vulnerability to trade-related disruptions. The relationship between sufficiency and diversity is not homogeneous, highlighting that some production systems are reliant on very few products.

Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment

Manual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system. This paper proposes an accurate automatic scoring model for Chinese-English interpretation via a multi-indicator assessment. From the three dimensions (i.e. keywords, content, and grammar) of the scoring rubrics, three improved attention-based BiLSTM neural models are proposed to learn the text of the transcribed responses. In the feature vectorisation stage, the pre-training model Bert is utilised to vectorise the keywords and content, and a random initialisation is used for the grammar. In addition, the fluency is also taken into account based on the speech speed. The overall holistic score is obtained by fusing the four scores using the random forest regressor. The experimental results demonstrate that the proposed scoring method is effective and can perform as good as the manual scoring.

Increasing Farm Size and Optimized Agronomic Practices to Improve Agricultural Sustainability: A Multi-Indicator Assessment in the North China Plain

Increasing Farm Size and Optimized Agronomic Practices to Improve Agricultural Sustainability: A Multi-Indicator Assessment in the North China Plain

Method of Ecological Risk Assessment for Land Use Based on Remote Sensing Image

Ecological risk assessment for land use can provide the basis for scientific evaluation of the impact of current human activities on land and its ecological effects. For the issue on ecological risk of land use, the method of ecological risk assessment for land use based on remote sensing image is proposed. Using remote sensing image as the main data of land use change and ecological effects, the land use situation is classified by pre-processing and analysis of remote sensing image. Combined with the estimation of solar radiation, spatial interpolation of meteorological data, atmospheric correction and geometric fine correction operations, remote sensing data is processed to improve the classification accuracy of the target area. According to the remote sensing image of land use and the analysis results of the data, as well as the dynamic model and the comprehensive evaluation conceptual model for land use, the multi-indicator assessment framework of ecological risk for land use is designed and the risk indicator system is constructed. Furthermore, the ecological risk assessment for land use in the entire region and sub-regions is completed. The research results show that the proposed method can effectively realize the ecological risk assessment for land use in the study area, which has good robustness.

Synergies and trade-offs for climate-resilient agriculture in India: an agro-climatic zone assessment

Globally, agriculture is recognized as a highly vulnerable sector to climate change and risks from climatic aberrations pose an imminent danger to the food security and sustainability of livelihoods. To bring robustness in climate adaptation planning, evaluation of resilience across homogenous regions is essential for developing and scaling suitable location-need-context specific interventions and policies that build the resilience of the agricultural system. In this paper, we present an analysis and discussion of multi-scalar and multi-indicator assessment, by profiling resilience across agro-climatic zones of India, based on the development of a Climate-Resilient Agriculture Index embracing environmental, technological, socio-economic, and institutional and infrastructural dimension. A total of 26 indicators, spread across these four dimensions, were employed to purport inter- and intra-agro-climatic zone differentials in the level of resilience. Among the zones, it was found that West Coast Plains & Ghats and Tans-Gangetic Plains had the highest degree of resilience to manage climate risks. Most of the districts lying within Eastern Himalayan Region, Middle Gangetic Plains, Eastern Plateau & Hills, and Western Dry Region had a lower degree of resilience. The study places greater emphasis on deciphering region-specific drivers and barriers to resilience at a further disaggregated scale for improving rural well-beings. It is construed that devising action plans emphasizing awareness, preservation of natural resources, diversification, building physical infrastructure, strengthening of grass-root institutions, and mainstreaming climate adaptation in the developmental policy is crucial for climate-resilient pathways.

A Data-Driven Gross Domestic Product Forecasting Model Based on Multi-Indicator Assessment

Gross domestic product (GDP) is a general reference to comprehensive measure the level of a country or region’s economic development and diagnoses the health of economy. Traditional economic census-based methods for GDP forecasting are often expensive and resource-consuming, more importantly, economic census results lag significantly. This paper presents a data-driven GDP forecasting model that integrates multidimensional data from the aspects of electricity consumption, climate and human activities. Specifically, the model is built up based on the long-short-term-memory neural network with particle swarm optimization algorithm. The input multidimensional data are analyzed by correlation-based feature selection, and then filtered to five influencing factors. The experimental results show that these influencing factors are obviously related to economic development, at the same time, GDP can be well predicted based on the proposed model in a timely and relatively accurate manner.

A Bottleneck Degree-Based Migrating Birds Optimization Algorithm for the PCB Production Scheduling

To address the printed circuit board shop scheduling problem, a dynamic based on multi-indicator bottleneck degree is proposed to formulate this complex flexible job shop scheduling problem (FJSP). Then, a bottleneck degree-based migrating birds optimization algorithm (MBO) is proposed to tackle this NP-hard problem. Specifically, a multi-indicator assessment model is first developed to obtain the bottleneck degree, and then a two-layer code-decoding structure based on the variable domain structure and the addition of the improved migratory bird algorithm with competitive mechanisms are employed to improve the effectiveness. Finally, the computational experiments demonstrate that the proposed dynamic scheduling approach based on bottleneck degrees and improved migrating bird algorithms can obtain promising results and it outperforms several other algorithms.

Multi-Indicator Assessment of Innovative Small-Scale Biomethane Technologies in Europe

Innovative small-scale biogas plants, including upgrading solutions to affordable biomethane, are necessary to tap into the spatially distributed potentials of organic waste. This research identified and assessed novel small-scale technologies before market-entry maturity in the key process steps of the biomethane chain. We assessed technical, economic, and ecological indicators, and compared them to larger-scale references. The assessment included 7 pre-treatment, 13 digester, and 11 upgrading systems all at the small scale. We collected recently available data for Europe (2016–2018) for small-scale technologies (<200 m3; raw biogas per hour). In the literature we did not find such a comprehensive assessment of actual European small-scale innovative non-market-ready technologies for the production of biomethane. Several conclusions were drawn for each of the individual process steps in the biomethane chain, e.g., the economic indicator calculated for the upgrading technologies shows that the upgrading costs, for some of them, are already close to the larger-scale reference (about 1.5 €ct/kWh raw biogas). Furthermore, biomethane production is absolutely context-specific, which dramatically limits the traditional way to evaluate technologies. Hence, new ways of integration of the technologies plays a major role on their future R&D.

A multi-indicator assessment of peri-urban agricultural production in Beijing, China

Urbanization has proceeded rapidly in China, bringing large-scale social, economic and environmental change to the strategically important peri-urban regions that surround expanding cities. In the Beijing Metropolitan Region, with an area of 16,411 km2, government policies have sought the development of multi-functional peri-urban agriculture, favoring the production of cash crops, and the area of vegetable, fruit and organically grown cereal production has increased, whereas the area under conventional cereal production has declined. In this study, six characteristic agricultural practices in the peri-urban zone were compared in terms of energy use efficiency, economic and environmental performance. A multi-dimensional index was also constructed to compare overall performance. Conventional cereal cropping of spring maize (AP1) and winter wheat-summer maize (AP2) had the lowest energy inputs, highest energy use efficiency, and a positive net energy balance. However, cash crops (greenhouse watermelon-tomato, open-field peach; AP3 and AP4) and organically produced crops (Chinese chestnut, millet; AP5 and AP6) achieved substantially higher economic returns per ha cultivated. Fruit and vegetable crops also achieved much higher yields than conventionally grown cereals. Organically produced crops, although higher in value, achieved lower yields. Product carbon footprints ranged from 0.53 to 6.1 kg CO2eq kg−1 and were lowest for fruit and vegetables. Water consumption ranged from <0.1 L kg−1 for Chinese chestnut to 435 L kg−1 for the outputs of the winter wheat-summer maize rotation. When the various indicator results were combined using a multi-dimensional index, the ranking of the different peri-urban agricultural practices was AP6 > AP5 > AP3 > AP1 > AP4 > AP2. These results demonstrate that the multiple functions of per-urban agriculture are not always complementary. However, the adjustment of peri-urban agricultural practices in the Beijing Metropolitan Region appears to be increasing overall benefits. Nevertheless, benchmarking against practices in other parts of China and the world, suggests that there is substantial scope for improvement of peri-urban agriculture in the Beijing Metropolitan Region, with excessive N fertilization identified as a major contributing factor for the performance gap. The development of peri-urban agriculture in Beijing, as elsewhere, has emphasized multi-functionality, providing local food supply, economic returns for local farmers, ecological functioning, along with amenity. Different agricultural practices provide these benefits in different ways. Assessments that address only a single aspect risk informing decisions that have adverse unintended consequences. Studies employing a variety of indicators across the social, economic and environmental dimensions are required if the potential benefits of peri-urban agriculture are to be realized.

Multi-indicator assessment of a water-saving agricultural engineering project in North Beijing, China

The Paddy Land-to-Dry Land (PLDL) program was a 10-year long (2006–2015) agricultural engineering program, initiated jointly by Beijing and Heibei Province, that was designed to increase urban water availability and improve household livelihoods in local farming communities. The program, supported by government subsidy, involved conversion of paddy (Oryza sativa) to maize (Zea mays). In this study, a range of environmental, social and economic indicators were used to evaluate the program. In relation to its primary goal of saving water, the program was considered successful, with water consumption of maize production 47% lower. The subsidy was also found to be influential in encouraging household participation. However, the small size of household farms and the small proportion of household revenues coming from agricultural production meant that the PLDL program did little to directly improve household livelihoods. Environmental indicators, assessed using life cycle assessment, showed mixed results. Although most indicators improved, some deteriorated, namely acidification and eutrophication potential. This highlights the importance of assessing environmental performance comprehensively to avoid burden shifting. Important environmental impacts could potentially have been reduced if the program had provided additional technological support to farmers. Nevertheless, the aggregated LCA results suggested that overall environmental performance was improved. The PLDL program could also be viewed as a success from the perspective that the monetarized ecosystem service benefits greatly exceeded the subsidy payment. An important spillover benefit was that participating households were able to achieve greater participation in non-farm employment, which contributed to higher household incomes and poverty alleviation. An important finding of this study is that an agricultural engineering program designed to alleviate water scarcity can have wide ranging environmental, economic and social impacts.

Assessing fire impacts on the carbon stability of fire-tolerant forests.

The carbon stability of fire-tolerant forests is often assumed but less frequently assessed, limiting the potential to anticipate threats to forest carbon posed by predicted increases in forest fire activity. Assessing the carbon stability of fire-tolerant forests requires multi-indicator approaches that recognize the myriad ways that fires influence the carbon balance, including combustion, deposition of pyrogenic material, and tree death, post-fire decomposition, recruitment, and growth. Five years after a large-scale wildfire in southeastern Australia, we assessed the impacts of low- and high-severity wildfire, with and without prescribed fire (≤10yr before), on carbon stocks in multiple pools, and on carbon stability indicators (carbon stock percentages in live trees and in small trees, and carbon stocks in char and fuels) in fire-tolerant eucalypt forests. Relative to unburned forest, high-severity wildfire decreased short-term (five-year) carbon stability by significantly decreasing live tree carbon stocks and percentage stocks in live standing trees (reflecting elevated tree mortality), by increasing the percentage of live tree carbon in small trees (those vulnerable to the next fire), and by potentially increasing the probability of another fire through increased elevated fine fuel loads. In contrast, low-severity wildfire enhanced carbon stability by having negligible effects on aboveground stocks and indicators, and by significantly increasing carbon stocks in char and, in particular, soils, indicating pyrogenic carbon accumulation. Overall, recent preceding prescribed fire did not markedly influence wildfire effects on short-term carbon stability at stand scales. Despite wide confidence intervals around mean stock differences, indicating uncertainty about the magnitude of fire effects in these natural forests, our assessment highlights the need for active management of carbon assets in fire-tolerant eucalypt forests under contemporary fire regimes. Decreased live tree carbon and increased reliance on younger cohorts for carbon recovery after high-severity wildfire could increase vulnerabilities to imminent fires, leading to decisions about interventions to maintain the productivity of some stands. Our multi-indicator assessment also highlights the importance of considering all carbon pools, particularly pyrogenic reservoirs like soils, when evaluating the potential for prescribed fire regimes to mitigate the carbon costs of wildfires in fire-prone landscapes.

Multi-indicator sensitivity analysis of climate change effects on landscapes in the Kiskunság National Park, Hungary

A multi-indicator approach assessing sensitivity to drought within different landscape types was explored within the Kiskunság National Park (Hungary) and its surrounding landscapes. The National Park preserves alkaline lakes, sand dunes, wetlands, dry steppes and forests, surrounded by a matrix of intensively used agricultural land and forests. The investigated indicators rely to soil moisture regime, changes in groundwater resources, biomass production of vegetation and wind erosion hazard. The study also estimated future drought hazard as an indicator of climate change (CC) by REMO and ALADIN regional climate model simulations applying two future time periods (2021–2050 and 2071–2100). Overlaying analysis of future CC scenarios and the multi-indicator assessment indicates increasing drought hazards over the whole area investigated, with landscapes in the northern part of the territory relatively more exposed. On the basis of the calculated indicators, the most sensitive areas were identified as being located in the areas of highest altitude and within the sandy area/alluvial plain transitional zone, which are mostly wetland and sand-dune regions. Results indicate that conservation management should especially focus on the northern part of the Kiskunság as an area most at risk of increasing drought. The outcomes of this research demonstrate the utility of a dynamic, multi-indicator landscape sensitivity approach to developing strategies to adapt on the multilayered and complex effects of CC on nature conservation practice.

Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system

There is an increasing need of effective monitoring systems for habitat quality assessment. Methods based on remote sensing (RS) features, such as vegetation indices, have been proposed as promising approaches, complementing methods based on categorical data to support decision making.Here, we evaluate the ability of Earth observation (EO) data, based on a new automated, knowledge-driven system, to predict several indicators for oak woodland habitat quality in a Portuguese Natura 2000 site.We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitats of a landscape undergoing agricultural abandonment. Forty-three predictors were calculated, and a multi-model inference framework was applied to evaluate the predictive strength of each data set for the several quality indicators.Three indicators were mainly explained by predictors related to landscape and neighbourhood structure. Overall, competing models based on the products of the automated knowledge-driven system had the best performance to explain quality indicators, compared to models based on manually classified land cover data.The system outputs in terms of both land cover classes and spectral/landscape indices were considered in the study, which highlights the advantages of combining EO data with RS techniques and improved modelling based on sound ecological hypotheses. Our findings strongly suggest that some features of habitat quality, such as structure and habitat composition, can be effectively monitored from EO data combined with in-field campaigns as part of an integrative monitoring framework for habitat status assessment.