Despite a recent surge of empirical studies examining associations between corporate environmental performance (or, more broadly, corporate social responsibility) and financial performance, researchers have been unable to give an integrated account of the mechanisms linking those environmental performances to financial outcomes. The practical implications of this research stream thus remain limited, offering managers little guidance in their efforts to improve environmental and financial performance. This study therefore examines the specific roles played by firms’ value-creating environmental practices and how these practices may help explain their environmental and financial performance. Drawing on 18,743 plant-level panels of detailed environmental information and their corresponding 455 firm-level panels of financial information, we investigate the relationship between a range of pollution prevention activities and environmental performance in the U.S. chemical industry and their influence on financial performance, particularly on cost competitiveness and market valuation. Our analysis finds that more-intensive pollution prevention activities, particularly those involving process efficiency and raw material management, result not only in superior environmental performance but in improved cost competitiveness, though not necessarily in higher market valuation.
Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD designs and evaluates their engineering performance. The proposed framework comprises seven stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of experiment in latent space, (4) CAD automation, (5) CAE automation, (6) transfer learning, and (7) visualization and analysis. The proposed framework is demonstrated through a road wheel design case study and indicates that AI can be practically incorporated into an end-use product design project. Engineers and industrial designers can jointly review a large number of generated 3D CAD models by using this framework along with the engineering performance results estimated by AI and find conceptual design candidates for the subsequent detailed design stage.
In response to the call for more research on impactful green IS, this paper examines one country-level environmental information disclosure system (EIDs), the U.S. Toxic Release Inventory (TRI), as a strategic tool or infrastructure of green IS and empirically tests the impact of environmental performance on financial performance in the chemical industry using both real financial and forward-looking measures. In particular, the study explores EDIs-inspired environmental managerial efforts and their influence on firms' financial performance. The study's finding that firms with better environmental performance also show greater cost competitiveness challenges the view that environmental managerial activities constitute an additional burden irrespective of firms' business operations. Instead, it suggests, these can be innovative activities that improve a firm's production efficiency, material resource management, and financial performance and sustainability.
The recent rapid transition in energy markets and technological advances in demand-side interventions has renewed attention on consumer behavior. A rich literature on potential factors affecting residential energy use or green technology adoption has highlighted the need to better understand the fundamental causes of consumer heterogeneity in buildings’ energy-related behavior. Unresolved questions such as which consumers are most likely to opt into demand-side management programs and what factors might explain the wide variation in behavioral responses to such programs make it difficult for policy-makers to develop cost-effective energy efficiency or demand response programs for residential buildings. This study extends the literature on involvement theory and energy-related behavior by proposing a holistic construct of household energy involvement (HEI) to represent consumers’ personal level of interest in energy services. Based on a survey of 5487 Korean households, it finds that HEI has a stronger association with consumer values, such as preferences for indoor thermal comfort and automation, than with socioeconomic or housing characteristics and demonstrates HEI’s potential as a reliable, integrated predictor of both energy consumption and energy-efficient purchases. The study illuminates the multifaceted influences that shape energy-related behavior in residential buildings and offers new tools to help utility regulators identify and profile viable market segments, improve the cost-effectiveness of their programs, and eventually promote urban sustainability.
Studies on manufacturing cost prediction based on deep learning have begun in recent years, but the cost prediction rationale cannot be explained because the models are still used as a black box. This study aims to propose a manufacturing cost prediction process for 3D computer-aided design (CAD) models using explainable artificial intelligence. The proposed process can visualize the machining features of the 3D CAD model that are influencing the increase in manufacturing costs. The proposed process consists of (1) data collection and pre-processing, (2) 3D deep learning architecture exploration, and (3) visualization to explain the prediction results. The proposed deep learning model shows high predictability of manufacturing cost for the computer numerical control (CNC) machined parts. In particular, using 3D gradient-weighted class activation mapping proves that the proposed model not only can detect the CNC machining features but also can differentiate the machining difficulty for the same feature. Using the proposed process, we can provide a design guidance to engineering designers in reducing manufacturing costs during the conceptual design phase. We can also provide real-time quotations and redesign proposals to online manufacturing platform customers.
To improve the reliability of energy system operations, utilities are increasingly addressing ways to promote the active and timely deployment of demand-side resources. Although previous literature has confirmed that dynamic pricing is effective for managing electricity peak demand, it presents complexity challenges attempting to modify households’ consumption decisions. This study improves our understanding of households’ decision-making processes and limitations, suggesting the possible role that information about behavioral alternatives and payoffs, decision-relevant information, can play in unlocking demand-side flexibility. We draw on the NK model, perform stylized simulations to propose a micro-level foundation for households’ adaptive search and role of decision-relevant information, and conduct a randomized field experiment to put these insights into practice under a dynamic tariff setting. While the households indeed curtail peak demand, those received such information exhibit a three standard-deviation greater demand response without reducing overall daily consumption. Confirming our simulation results, the individual households’ demand response also differs by their pre-experimental consumption patterns, with the effect of the informational support diminishing over time. Utility planners are encouraged to provide decision-relevant information not only to reduce peak demand but also to enhance public acceptance of dynamic pricing by promoting load-shifting that alleviates discomfort from reduced electricity consumption.
Research regarding design automation that integrates artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework that automatically generates three-dimensional (3D) CAD models, predicts CAE results immediately, explains the results, and verifies the reliability. The proposed framework comprises seven stages: (1) two-dimensional (2D) generative design, (2) dimensionality reduction, (3) design of experiment in latent space, (4) CAD automation, (5) CAE automation, (6) transfer learning, and (7) visualization and analysis. The proposed framework is demonstrated through a road wheel design case study and indicates that AI can be practically incorporated into end-use product design. Using this framework, it is expected that industrial designers and engineers can jointly review feasible engineering 3D CAD models created by AI and select the best design for the market in the early stages of product development. In addition, because the proposed deep learning model can predict CAE results based on 2D disc-view design, industrial designers can obtain instant feedback regarding the engineering performance of 2D concept sketches.
In recent research on the formulation prediction of oral dissolving drugs, deep learning models with significantly improved performance compared to machine learning models were proposed. However, the performance degradation due to limitations of an imbalanced dataset with a small size and inefficient neural network structure has still not been resolved. Therefore, we propose new deep learning-based prediction models that maximize the prediction performance for disintegration time of oral fast disintegrating films (OFDF) and cumulative dissolution profiles of sustained-release matrix tablets (SRMT). In the case of OFDF, we use principal component analysis (PCA) to reduce the dimensionality of the dataset, thereby improving the prediction performance and reducing the training time. In the case of SRMT, the Wasserstein generative adversarial network (WGAN), a neural network-based generative model, is used to overcome the limitation of performance improvement due to the lack of experimental data. To the best of our knowledge, this is the first work that utilizes WGAN for pharmaceutical formulation prediction. Experimental results show that the proposed methods have superior performance than the existing methods for all the performance metrics considered.
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