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Current literature presents limited measurement methods of quantifying manufacturers' performance with environmental concerns. The purpose of this paper is to construct a company performance index for benchmarking motor vehicle manufacturers (MVMs) with environmental concerns.

Methods of constructing the index include regression analysis, a modified linear method for normalizing variables and a geometric mean for aggregating variables into a single index I_MVM (index for MVMs). A case study is conducted in 12 MVMs from 2008 to 2017. A sensitivity analysis with the simple additive weighting method is performed to analyze how different aggregation methods affect the final value. The index I_MVM is assessed through a benchmark with three existing indices.

Three realistic considerations are identified from MVMs, based on which proper and transparent methods are chosen to construct the I_MVM. The construction of the index I_MVM has been assessed through a benchmark against the methodologies of three other indices. The results indicate that the new measurement is feasible and effective for MVMs to measure their company performance from an environmental perspective.

The construction of the index I_MVM can support policymakers with accurate statistics for decision-making. As a response to current imperative climate policies, this paper raises awareness of CO₂ emissions in vehicles' production. For statistical organizations and stakeholders in the investment world, this paper provides available and reliable statistics for trend analysis of different MVMs.

A new method is designed for constructing a company performance index for MVMs. Three environmental variables are identified based on literature, their environmental impact as well as their data availability from public documents. A ranking by manufacturer with environmental concerns is generated. This index can contribute with available statistics and useful insights toward decision-making.

Deepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.

The lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.

The MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m³ in 2001 to 16.73 million m³ in 2011 and further declined to 15.35 million m³ in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.

None of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.

The Public–Private Partnership (PPP) mode has been widely used in the urban water environment treatment project (UWETP). The public is the direct perceiver of urban water environment treatment PPP project (UWETP-PPP), and their satisfaction with it can reflect the project's success to some extent. The purpose of this paper is to provide operational management strategies for special-purpose vehicle (SPV) company through public satisfaction evaluation of UWETP-PPP.

A questionnaire was designed through the literature review and experts' opinions that consisted of 33 indexes to investigate public satisfaction with UWETP-PPP. Then, the exploratory factor analysis (EFA) was used to confirm the evaluation index system of public satisfaction. Next, the weights of the indexes were calculated by the normalized mean method and fuzzy synthetic evaluation (FSE) was applied to evaluate public satisfaction with UWETP-PPP. Finally, the importance-performance analysis (IPA) was used to suggest improvement strategies of public satisfaction with UWETP-PPP.

The factors affecting public satisfaction with UWETP-PPP are preliminarily determined, and an evaluation model that provides a specific basis for evaluating public satisfaction with UWETP-PPP in the future is constructed. Furthermore, some strategies and suggestions to improve the performance level of UWETP-PPP are put forward.

First, the factors influencing public satisfaction with UWETP-PPP are numerous and complex. The factors discussed in this study may be further refined to make the research more reliable and effective. Second, the sample from this study can be expanded to multiple projects, so as to make the study more general. Finally, given that the influencing factors in the paper were elicited by using scores obtained through a questionnaire survey, it would bring bias to the result to some extent. The evaluation method can be further improved, such as combining with interval intuitionistic fuzzy evaluation.

In the context of the characteristics of PPP projects in water environment treatment and the huge demand of the country to apply PPP model in the territory of water environment treatment, this study evaluated the public satisfaction with UWETP-PPP in Xuchang City, China. The result provided a beneficial reference for the private organizations to take appropriate actions to improve the public satisfaction level of UWETP-PPP, so as to improve the performance level of SPV. It also provided a system of public satisfaction evaluation with UWETP-PPP, which can be considered as the standard of public satisfaction survey, thus improving the comprehensiveness of the UWETP-PPP performance evaluation system and the government regulatory capacity, thus affecting the sustainable development of UWETP-PPP.

In addition, the research results provide a useful reference for the public organizations to issue a UWETP-PPP regulation rule, review a UWETP-PPP design plan, and conduct new similar project decision-making. In summary, the study results not only serve as a good reference for the evaluation of public satisfaction of eco-environmental PPP projects, but also have essential application value for improving the service level, performance improvement and operational management of the SPV.

The research results of this paper have certain wide-spread value, and the research framework and research method can be applied to other infrastructure section, such as transportation, stadium et al.

The purpose of this paper is to provide a picture of the water situation of the states of India and to identify key areas in which intervention is necessary for sustainable development and poverty elevation.

To understand the trend and situation of water across the states, Water Poverty Index (WPI) has been constructed. WPI has been computed for the years 2012 and 2018 to get a picture of temporal change happening in the region. Further, descriptive statistics were used to show the required changes.

Jharkhand and Rajasthan continue to be the worst performer in both time periods. Water poverty was the least in the states of Goa and Chandigarh for both time periods. Although owing to improvement in access and capacity component, the water status of India as a whole improved from 2012 to 2018 but few states have witnessed a decline in their water situation mainly due to deterioration in the environment and resource components.

This paper adds to the relatively scarce literature on the water situation conducted for the states of India. The findings of the paper provide insights into the lacking areas responsible for the deterioration in water poverty status. The results can be utilized for framing proper policies to combat the water woes of the country.

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.

To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.

The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.

To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

Large deformation problems are frequently encountered in various fields of geotechnical engineering. The particle finite element method (PFEM) has been proven to be a promising method to solve large deformation problems. This study aims to develop a computational framework for modelling the hydro-mechanical coupled porous media at large deformation based on the PFEM.

The PFEM is extended by adopting the linear and quadratic triangular elements for pore water pressure and displacements. A six-node triangular element is used for modelling two-dimensional problems instead of the low-order three-node triangular element. Thus, the numerical instability induced by volumetric locking is avoided. The Modified Cam Clay (MCC) model is used to describe the elasto-plastic soil behaviour.

The proposed approach is used for analysing several consolidation problems. The numerical results have demonstrated that large deformation consolidation problems with the proposed approach can be accomplished without numerical difficulties and loss of accuracy. The coupled PFEM provides a stable and robust numerical tool in solving large deformation consolidation problems. It is demonstrated that the proposed approach is intrinsically stable.

The PFEM is extended to consider large deformation-coupled hydro-mechanical problem. PFEM is enhanced by using a six-node quadratic triangular element for displacement and this is coupled with a four-node quadrilateral element for modelling excess pore pressure.

The purpose of this paper is to present the results of research into using an additive to SAE 15W/40 engine oil during operation and its influence on lubricating properties (normalised tests) on weld point Pz, non-seizure load Pn, load wear index Ih and on seizure load Pt. The friction pair consisted of a group of four balls and the tested lubricant. Moreover, the author tested the influence of an additive to engine oil (non-normalised tests) on tribological properties, including friction force, wear and the temperature of friction area for the C45 steel/210Cr12 steel friction joint. She also determined the influence of an additive to engine oil on the formation of the operating surface layer. The research results helped to build the model of the boundary layer that was formed as a result of adding an additive to engine oil.

The lubricant properties of engine oil and engine oil to which an additive was added during operation were determined according to PN-76/C-04147. The following are the indexes of lubricant properties: weld point Pz, load wear index Ih, non-seizure load Pn, seizure load and average scar diameter. The Pz, Pn and Ih indexes were determined at abruptly increasing load to the moment of welding of the friction pair. The Pt index was determined at the increasing load of the friction pair from 0 to 800 daN at the speed of 408.8 N/s. The tests of tribological properties (friction force, wear and the temperature of friction area) were conducted for the C45/210 Cr12 friction pair in the presence of a lubricant and a lubricant with an additive.

The modification of SAE 15W/40 engine oil with the additive added during operation resulted in improved indexes of lubricant properties Pz, Pn, Ih and Pt and average scar diameter. The boundary layer for the modified oil breaks after a longer time and at lesser friction force. The modification of the engine oil reduced the wear of the friction pair. After the friction process, element composition in the surface layer of the wear trace and its distribution were determined in relation to applied lubricants. A significant amount of sulphur, phosphorus and oxygen, as well as an insignificant amount of copper, was observed in the wear trace after the friction process in the presence of the lubricant medium. The distribution of elements in the wear trace when the engine oil with the additive was used is steady in the wear trace and outside it. Some sulphur, phosphorus and chlorine were found in the wear trace.

The results of tests on tribological properties (non-normalised tests) confirmed the positive affect of the additive to engine oil on lubricant properties (normalised tests). The modification of the engine oil caused reduced friction force and the reduced wear of the friction pair. The reduction of friction force and wear was the result of the formation of the surface of a greater amplitude density of unevenness tops in the friction process. Moreover, the operating surface layer, created in the friction process when the additive was added to the engine oil, had greater load participation at 50 per cent C. This operational surface layer improved tribological properties, i.e. it reduced value of friction force and wear. The test results were used to build a model of the boundary layer created as a result of the additive added to engine oil.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.