Poor Environmental Performance Research Articles

Lessons from the Pilot Project of Korean ETS on the Local Landscape of Economy

For a sustainable landscape of local economies, many researchers have emphasized the importance of field-oriented differentiation in government policies. In particular, the Paris Agreement, based on the bottom-up approach, aims to maximize the participation of all economic agents, in contrast to the top-down approach of the Tokyo Protocol. In response to these global paradigm shifts in the local landscape, local governments in Korea have made significant efforts to adapt to sustainable development during the pilot phase of emission trading scheme (ETS), during the period from 2015 to 2020. This study evaluates the performance of these local government policies in the transition to a carbon-zero economy. Using the general non-radial directional distance function (GNDDF), we found that Gyeongsang Province demonstrated enhanced environmental total factor productivity (TFP) during the pilot project, whereas the Seoul metropolitan area lagged behind due to a lack of governance. As the economic center of Korea, Seoul showed poor environmental performance because of the arbitrary elimination of green belt areas and unchecked land development, resulting in environmental degradation, a trend common in many developing countries facing climate adaptation challenges. To address these urbanization issues, this study concludes that a balanced approach combining stricter regulations with market-oriented promotional incentives is essential for optimizing the transition of local economies to a sustainable landscape.

Evaluation of visual SLAM algorithms in unstructured planetary-like and agricultural environments

Given the significant advance in visual SLAM (VSLAM), it might be assumed that the location and mapping problem has been solved. Nevertheless, VSLAM algorithms may exhibit poor performance in unstructured environments. This paper addresses the problem of VSLAM in unstructured planetary-like and agricultural environments. A performance study of state-of-the-art algorithms in these environments was conducted to evaluate their robustness. Quantitative and qualitative results of the study are reported, which exposes that the impressive performance of most state-of-the-art VSLAM algorithms is not generally reflected in unstructured planetary-like and agricultural environments. Statistical scene analysis was performed on datasets from well-known structured environments as well as planetary-like and agricultural datasets to identify visual differences between structured and unstructured environments, which cause VSLAM algorithms to fail. In addition, strategies to overcome the VSLAM algorithm limitations in unstructured planetary-like and agricultural environments are suggested to guide future research on VSLAM in these environments.

Study on stability and characterization of high internal phase water-in-oil compound collector emulsion

To address the high consumption and poor environmental performance associated with traditional coal slime flotation oils, we have developed a high internal phase water-in-oil (HIP W/O) emulsion using methyl oleate and methyl laurate as collectors. This study examines the factors influencing emulsion stability and water dispersion. Comparative flotation tests were performed, and the emulsion’s microstructure and rheological properties were assessed using freeze-scanning electron microscopy and a rheometer. The optimal emulsion formulation contained 5.56 vol% of a compound collector, 4.44 vol% Span 80, and 90 vol% of a sodium chloride and gelatin water solution. The incorporation of gelatin into the water phase established a three-dimensional gel network within the emulsion droplets, significantly enhancing both viscosity and stability. This emulsion reduced compound collector consumption by approximately 84 % while maintaining comparable flotation performance. Its fine particle distribution and effective coal surface adsorption highlight its potential as an environmentally friendly option for fine coal flotation, thus promoting cleaner and more efficient coal utilization.

Leak Event Diagnosis for Power Plants: Generative Anomaly Detection Using Prototypical Networks.

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset.

New media surveillance, environmental information uncertainty and corporate environmental information disclosure

This paper selects A-share heavy polluting enterprises in China's Shanghai and Shenzhen cities from 2009 to 2022. The following conclusions are obtained from the empirical evidence: new media monitoring can encourage enterprises to disclose information. Environmental information uncertainty can encourage enterprises to disclose information. The positive effect of environmental information uncertainty on environmental information disclosure is more significant for firms with poorer environmental performance than those with better environmental performance. The effect of the new media monitoring on environmental information disclosure of non-state-owned enterprises is more significant when other information is held constant.

MonoLoT: Self-Supervised Monocular Depth Estimation in Low-Texture Scenes for Automatic Robotic Endoscopy.

The self-supervised monocular depth estimation framework is well-suited for medical images that lack ground-truth depth, such as those from digestive endoscopes, facilitating navigation and 3D reconstruction in the gastrointestinal tract. However, this framework faces several limitations, including poor performance in low-texture environments, limited generalisation to real-world datasets, and unclear applicability in downstream tasks like visual servoing. To tackle these challenges, we propose MonoLoT, a self-supervised monocular depth estimation framework featuring two key innovations: point matching loss and batch image shuffle. Extensive ablation studies on two publicly available datasets, namely C3VD and SimCol, have shown that methods enabled by MonoLoT achieve substantial improvements, with accuracies of 0.944 on C3VD and 0.959 on SimCol, surpassing both depth-supervised and self-supervised baselines on C3VD. Qualitative evaluations on real-world endoscopic data underscore the generalisation capabilities of our methods, outperforming both depth-supervised and self-supervised baselines. To demonstrate the feasibility of using monocular depth estimation for visual servoing, we have successfully integrated our method into a proof-of-concept robotic platform, enabling real-time automatic intervention and control in digestive endoscopy. In summary, our method represents a significant advancement in monocular depth estimation for digestive endoscopy, overcoming key challenges and opening promising avenues for medical applications.

Limiting environmental reporting flexibility: investor judgment based on the EU taxonomy

This experimental research investigates the effect of different types of environmental information on investor judgment. By examining three experimental cases varying the level of environmental disclosure, we evaluate the investment judgments of professional (Study 1) and private German investors (Study 2). Primarily, we investigate whether traditional, commonly disclosed environmental information affects investor judgments. Furthermore, we explore the effects of linking non-financial reporting elements to quantitative financial measures through the EU taxonomy by adding taxonomy indicators. Specifically, we operationalized the case where companies fall into a category of poor environmental performance by taxonomy classification. We find that only traditional environmental disclosure in combination with standardized taxonomy-aligned information (below average), influences the investment judgment. However, professional investors exhibit a significantly negative response, while private investors show a significantly positive reaction when constraining reporting flexibility through the inclusion of standardized taxonomy measures with poor performance. Consequently, we conclude that the connection between non-financial reporting elements and quantitative standardized financial measures enhances transparency for professional investors. Private investors, on the other hand, reward additional taxonomy-aligned environmental information irrespective of its content. This implies that environmental information generally conveys positive signals to private investors, but uncertainty in investment judgment can be assumed.

Traditional Village Morphological Characteristics and Driving Mechanism from a Rural Sustainability Perspective: Evidence from Jiangsu Province

(1) Background: The sustainable development of rural areas has become a critical factor in global economic and social transformation. As an essential part of China’s rural ecological and cultural system, traditional villages are now facing a crisis of yearly decline, and sustainable development has become a meaningful way to solve the problem. This study utilized morphological indicator analysis and the SDGs as an evaluation framework to reveal the correlation and driving factors between traditional villages’ spatial form and sustainability indicators. From the perspective of the spatial form, this approach has specific reference significance for improving the sustainability of traditional villages. (2) Methods: A framework for detecting the driving factors of rural sustainability based on four dimensions (morphology, environment, economy, and society) was constructed. A geographic information system (GIS) was used to analyze the geographic patterns and morphological indicator characteristics of traditional villages in Jiangsu Province, and GeoDetector was used to analyze the driving mechanisms of the spatial patterns of sustainability in traditional villages, providing the basis for spatial zoning and differentiated policy design for the construction, planning, and management of sustainable villages. (3) Results: ➀ The spatial patterns and morphological characteristics of traditional villages exhibit prominent geographical imbalances and significant cluster cores. ➁ The high-density and low-aspect-ratio rural form in the southern region (where rural industries are developed) promotes good economic sustainability in rural areas but also leads to poor environmental performance. The rural areas in the southwest and north (high-density forest areas) have medium density and a high aspect ratio, and the lack of agricultural space and external connections affects their social performance. The main focus is on poverty reduction and urban cooperation. The central and northern lakeside areas and the eastern coastal areas (important ecological protection areas) have low density and high aspect ratios, which have helped them to achieve excellent environmental performance but also led to contradictions in environmental, economic, and social performance. Maintaining low-density patterns, using clean energy, and protecting terrestrial and underwater biodiversity are essential to the sustainability of the rural environment. The agglomeration of spatial patterns promotes cooperation between rural and urban areas and improves industrial development, contributing to the sustainability of the rural economy. Improving social welfare and agricultural development contributes to the sustainability of rural societies. ➂ The impacts of various factors vary significantly; for example, Life below Water (SDG14), Climate Action (SDG13), and No Poverty (SDG1) are the most prominent, followed by Partnerships for the Goals (SDG17), Affordable and Clean Energy (SDG7), and Recent Work and Economic Growth (SDG8). (4) Conclusions: It is recommended that the government, with the driving mechanisms, divide the spatial management zoning of traditional villages in Jiangsu into three types of policy areas: environmental-oriented, economic-oriented, and social-oriented. Differentiated and targeted suggestions should be proposed to provide a critical decision-making basis for protecting and utilizing traditional villages in Jiangsu and similar provinces, as well as to help promote rural revitalization and sustainable rural construction in China.

Determinants of corporate environmental performance and the moderating effect of economic crises

PurposeThis paper aims to identify the effect of environmental management systems (EMSs), commitment to stakeholders and gender diversity on corporate environmental performance (CEP) and the extent to which an economic crisis moderates these relationships.Design/methodology/approachA regression analysis was conducted on a sample of 14,217 observations from 1,933 firms from 26 countries from 2002 to 2010. The estimator used is ordinary least squares with heteroscedastic panel-corrected standard errors (PCSEs), which allows us to obtain consistent results in the presence of heteroscedasticity and autocorrelation.FindingsThe results show that EMSs and stakeholder engagement are mechanisms that drive CEP but lose their effectiveness in times of crisis. However, the presence of women on boards has a positive effect on CEP that is not affected by an economic crisis.Research limitations/implicationsThe study has some limitations that could be addressed in the future. We present board gender diversity as a governance mechanism because its role is strongly related to non-financial performance. Future studies could focus on other corporate governance mechanisms, such as the presence of institutional or long-term investors. In addition, other mechanisms could be found that can counteract poor environmental performance in times of crisis. Finally, it might be useful to contrast these results with the crisis generated by the coronavirus pandemic.Practical implicationsThe results obtained have important practical implications at the corporate and institutional levels. At the corporate level, they highlight, as essential contributions, that environmental management systems and stakeholder orientation are not effective in times of economic crisis, except for with the presence of women on the board.Social implicationsFollowing the crisis, the European Commission has promoted gender diversity on boards as a mechanism to improve the governance of entities – improving, among other aspects, sustainability. In this sense, another one of the practical implications of the study is support for the policies that the European Union has implemented over the last two decades.Originality/valueThe paper analyses how a crisis affects the moral and cultural institutional mechanisms that promote CEP. Gender diversity on the board of directors not only promotes environmental performance but also appears to be a governance mechanism that ensures this performance in times of crisis when the other mechanisms lose their effectiveness. The study proposes specific policies that help maintain environmental performance in an economic crisis.

Vision SLAM algorithm for wheeled robots integrating multiple sensors.

Wheeled robots play a crucial role in driving the autonomy and intelligence of robotics. However, they often encounter challenges such as tracking loss and poor real-time performance in low-texture environments. In response to these issues, this research proposes a real-time localization and mapping algorithm based on the fusion of multiple features, utilizing point, line, surface, and matrix decomposition characteristics. Building upon this foundation, the algorithm integrates multiple sensors to design a vision-based real-time localization and mapping algorithm for wheeled robots. The study concludes with experimental validation on a two-wheeled robot platform. The results indicated that the multi-feature fusion algorithm achieved the highest average accuracy in both conventional indoor datasets (84.57%) and sparse-feature indoor datasets (82.37%). In indoor scenarios, the vision-based algorithm integrating multiple sensors achieved an average accuracy of 85.4% with a processing time of 64.4 ms. In outdoor scenarios, the proposed algorithm exhibited a 14.51% accuracy improvement over a vision-based algorithm without closed-loop detection. In summary, the proposed method demonstrated outstanding accuracy and real-time performance, exhibiting favorable application effects across various practical scenarios.

Disclosure of CO2 emissions, community complaints, occupational safety and its relation to financial distress

The Indonesian government is committed to resolving emission issues through the development of green industries. The government encourages industries to transform into sustainable industries that are environmentally friendly. The Indonesian government asks the public and the market to put pressure on the industry to improve its environmental performance. The public can monitor and pressure companies through the disclosure of environmental performance information such as disclosure of CO2 emissions. In addition to environmental performance, disclosure of corporate social performance is no less important. Social performance relates to the company’s responsibility towards employees (work accidents) and the surrounding community (community complaints). The risk of companies that have poor environmental performance and social performance can result in the company experiencing financial distress. This study aims to examine the effect of disclosure of CO2 emissions, community complaints, and work safety on Financial Distress. Financial Distress is measured using the Altman Z Score and operational cash flow adequacy. This study uses age and company size as control variables. The sample of this study is manufacturing companies listed on the Indonesia Stock Exchange from 2018-2021. This study uses the Common Effect Model (CEM) and Random Effect Model (REM). The results of the research show Disclosure of CO2 emissions, community complaints, occupational safety has no significant effect on financial distress. However, firm age and firm size have a significant positive effect on the adequacy of the firm’s operating cash flow.

A Parametric Approach for Optimizing Design Solutions in Urban Regeneration and Reshaping: An Application to a District Block in Bologna, Italy

This paper addresses the deep renovation and enhancement of energy efficiency in existing urban areas, aiming to transform them into zero energy districts, utilizing renewable sources. This transformative process is essential to align with contemporary models for new buildings and to elevate existing structures to meet environmental housing standards mandated by current regulations. The proposed densification strategy is motivated by the necessity to augment the real estate value and enhance the architectural and performance quality of the existing building stock, all while minimizing additional land occupation. Its efficacy has been rigorously tested and demonstrated within the European Horizon 2020 project “ABRACADABRA”. The key actions advocated for in this approach revolve around the incorporation of renewable energies, volumetric expansion, and completion volumes (urban infill), within the Positive Energy District. The validation of this process occurs on a district scale in a social housing urban block located in Bologna (Italy), with currently poor environmental performance. A multi-criteria analysis was employed to examine the architectural, climatic, and energy context. The meta-design phase successfully attained the energy and economic targets set by the project and by local and European regulations settings, offering an optimized design solutions for reshaping the built environment.

CONTINUOUS BIM ALIGNMENT FOR MIXED REALITY VISUALISATION

Abstract. Several methods exist that can be used to perform initial alignment of Building information models (BIMs) to the real building for Mixed Reality (MR) applications, such as marker-based or markerless visual methods, but this alignment is susceptible to drift over time. The existing model-based methods that can be used to maintain this alignment have multiple limitations, such as the use of iterative processes and poor performance in environments with either too many or not enough lines. To address these issues, we propose an end-to-end trainable Convolutional Neural Network (CNN) that takes a real and synthetic BIM image pair as input to regress the 6 DoF relative camera pose difference between them directly. By correcting the relative pose error we are able to considerably improve the alignment of the BIM to the real building. Furthermore, the results of our experiments demonstrate good performance in a challenging environment and high resilience to domain shift between synthetic and real images. A high localisation accuracy of approximately 7.0 cm and 0.9° is achieved which indicates the method can be used to reduce the camera tracking drift for MR applications.

Too dark to see: Does board governance moderate the relationship between carbon emissions and analyst forecast errors?

AbstractThis study aims to demonstrate that carbon emissions may increase analyst forecast errors because poor environmental performance obscures prospects for business operations. We further examine whether strong board governance moderates such a relationship. Using a sample of S&P 500 firms from the Carbon Disclosure Project (CDP), we employ Heckman's two‐stage model to examine our research questions. We find a positive association between carbon intensity and forecast errors, but strong board governance, captured by board independence, board diligence, and committee size, may mitigate this positive relationship. However, the effect is amplified for firms engaged in controversial governance practices such as CEO duality and long CEO tenure. Our study provides insight for managers by raising concerns due to high levels of carbon emissions and demonstrating that different governance characteristics may alleviate the adverse effect of carbon emissions on forecast errors.

Numerical Study on Hydrodynamic Performance of a New Semi-Submersible Aquaculture Platform

To address the limitations of traditional aquaculture cages, such as poor performance in offshore environments, challenges in energy supply, and the inability to integrate modern farming facilities, a new semi-submersible aquaculture platform has been developed and put into operation. The hydrodynamic performance of the platform is analyzed using a fully coupled numerical model. The numerical model incorporates the main structure of the platform, the net system, and the mooring system, and is based on the linear potential flow theory and the Morison equation. The natural period of the platform is found to be far away from the common wave period. The mooring system for the platform in a specific sea area is proven to be safe. The net system significantly affects the hydrodynamic performance and alleviates the dynamic response of the platform. This study provides valuable insights for researchers and engineers in the design and optimization of this type of semi-submersible aquaculture platform in the future.

Do individual investors pay attention to the information acquisition activities of institutional investors?

This study investigates individual investors' responses to institutional investors' information acquisition activities. We examine the association between institutional investors' site visits and individual investors' posts on the online message board. We find that the total abnormal posts of individual investors significantly increase after institutional investors' site visits. Additional tests show that individual investors will pay more attention when institutional investors visit firms with low profitability, high earnings management, low manager sentiment, and poor environmental, social, and governance (ESG) performance.

A novel audio-visual based beamformer using a 360° camera and planar microphone array system

Beamforming systems that rely only on microphone arrays may suffer from poor performance in complex acoustic environments with multiple sources and room reverberation. To overcome this limitation, we propose a novel method that combines a planar microphone array and a 360° camera to steer the beamformer. The camera provides visual information about the location and orientation of sound sources, which can be used to adjust the beamformer parameters. We will present results that compare the signal-to-noise ratio (SNR) of the proposed method with a conventional beamforming microphone-based method in a simulated room with different source positions, orientations, and noise levels to investigate the benefit of visual information for beamforming in different environments. The proposed method has the potential to improve the performance of beamforming systems in applications such as speech enhancement, speech recognition, and speaker localization.

MVS‐SLAM: Enhanced multiview geometry for improved semantic RGBD SLAM in dynamic environment

AbstractSimultaneous Localization and Mapping (SLAM) is a crucial technology for intelligent mobile robots to operate successfully in unknown environments. While many excellent SLAM systems have been developed in recent years, most assume that the environment is static, resulting in poor performance in dynamic environments. To address this limitation, we propose multiview stereo (MVS)‐SLAM (MVS‐SLAM), a real‐time semantic RGBD SLAM system with improved‐multiview geometry, built on the RGBD mode of ORB‐SLAM3. MVS‐SLAM tightly integrates semantic and geometric information to tackle the challenges posed by dynamic scenes. To meet the real‐time requirements, the semantic module leverages the latest and fastest object detection network, YOLOv7, to provide semantic prior knowledge for the geometric module. We introduce a novel geometric constraint method that capitalizes on depth images and semantic information to recover three‐dimensional (3D) feature points and initial camera pose. We use a 3D coordinate error threshold to identify dynamic points and remove them using the K‐means clustering algorithm. This approach effectively reduces the impact of dynamic points. We validate MVS‐SLAM using challenging dynamic sequences from the TUM data set, demonstrating that it significantly improves localization accuracy and system robustness in all types of dynamic environments.

Impact of corporate governance diversity on carbon emission under environmental policy via the mandatory nonfinancial reporting regulation

AbstractThis study builds on the expanding literature on the interplay of corporate governance and corporate environment behaviour following the introduction of the carbon reporting directives of the UK Companies Act in 2013. We specifically focus on seeking clarity on the relationship between gender diversity, board independence, and board size with corporate environmental performance. The study examines these relationships under a mandatory nonfinancial reporting (NFR) requirement and tests the impact of regulatory shocks on board composition and channels affecting carbon emission. The findings confirm that board gender diversity and independence improve a firm's environmental performance. And while larger board sizes lead to larger environmental investments, the study finds that larger board sizes leads to poor environmental performance for the firm. The findings contribute to developments in countries, such as the United States, where there is an ongoing debate on the adoption of a mandatory NFR of carbon and the response of corporate boards.

SCE-SLAM: a real-time semantic RGBD SLAM system in dynamic scenes based on spatial coordinate error

Simultaneous localization and mapping (SLAM) is one of the prerequisite technologies for intelligent mobile robots to accomplish various tasks in unknown environments. In recent years, many excellent SLAM systems have emerged, but most of them have a basic assumption that the environment is static, which results in their poor performance in dynamic environments. To solve this problem, this paper presents SCE-SLAM: a novel real-time semantic RGB-D SLAM system that is built on the RGB-D mode of ORB-SLAM3. SCE-SLAM tightly combines semantic and geometric information. Considering the real-time requirements, the semantic module provides semantic prior knowledge for the geometric module using the latest and fastest object detection network YOLOv7. Then, a new geometric constraint method is proposed to filter dynamic feature points. This method takes full advantage of depth images and semantic information to recover three-dimensional (3D) feature points and the initial camera pose. A 3D coordinate error is used as a threshold, and SCE-SLAM removes dynamic points using the K-means clustering algorithm. In this way, SCE-SLAM effectively reduces the impact of dynamic points. Furthermore, we validate SCE-SLAM with challenging dynamic sequences of the TUM dataset. The results demonstrate that SCE-SLAM significantly improves the localization accuracy and system robustness in all kinds of dynamic environments.