We propose an evolutionary model to study the transition toward green technology under the influence of innovation. Clean and dirty technologies are selected according to their profitability under an environmental tax, which depends on the overall pollution level. Pollution itself evolves dynamically: it results from the emissions of the two types of producers, naturally decays, and is reduced through the implementation of the current abatement technology. The regulator collects tax revenues and allocates them between the implementation of the existing abatement technology and its innovation, which increases the stock of knowledge and thereby enhances abatement effectiveness. From a static perspective, we show the existence of steady states, both with homogeneous populations of clean or dirty producers and with heterogeneous populations where both technologies coexist. We discuss the mechanisms through which these steady states emerge and how they may evolve into one another. From a dynamical perspective, we characterize the resulting scenarios, showing how innovation can foster a green transition if coupled with a suitable level of taxation. At the same time, we investigate how im
In the artificial intelligence (AI) age, firms increasingly invest in AI technology innovation to secure competitive advantages. However, the relationship between firms' AI technology innovation and consumer complaints remains insufficiently explored. Drawing on Protection Motivation Theory (PMT), this paper investigates how firms' AI technology innovation influences consumer complaints. Employing a multimethod approach, Study 1 analyzes panel data from S&P 500 firms (N = 2,758 firm-year observations), Study 2 examines user-generated Reddit data (N = 2,033,814 submissions and comments), and Study 3 involves two controlled experiments (N = 410 and N = 500). The results reveal that firms' AI technology innovation significantly increases consumers' threat-related emotions, heightening their complaints. Furthermore, compared to AI process innovation, AI product innovation leads to higher consumer complaints. This paper advances the understanding of consumers' psychological responses to firms' AI innovation and provides practical implications for managing consumer complaints effectively.
This study investigates the relationship between corporate digital innovation and Environmental, Social, and Governance (ESG) performance, with a specific focus on the mediating role of Generative artificial intelligence technology adoption. Using a comprehensive panel dataset of 8,000 observations from the CMARS and WIND database spanning from 2015 to 2023, we employ multiple econometric techniques to examine this relationship. Our findings reveal that digital innovation significantly enhances corporate ESG performance, with GAI technology adoption serving as a crucial mediating mechanism. Specifically, digital innovation positively influences GAI technology adoption, which subsequently improves ESG performance. Furthermore, our heterogeneity analysis indicates that this relationship varies across firm size, industry type, and ownership structure. Finally, our results remain robust after addressing potential endogeneity concerns through instrumental variable estimation, propensity score matching, and differenc in differences approaches. This research contributes to the growing literature on technologydriven sustainability transformations and offers practical implications for corpo
This paper introduces the Environmental Justice in Technology (EJIT) Principles, a framework to help reorient technological development toward social and ecological justice and collective flourishing. In response to prevailing models of technological innovation that prioritize speed, scale, and profit while neglecting systemic injustice, the EJIT principles offer an alternative: a set of guiding values that foreground interdependence, repair, and community self-determination. Drawing inspiration from the 1991 principles of environmental justice, this framework extends their commitments into the technological domain, treating environmental justice not as a peripheral concern but as a necessary foundation for building equitable and regenerative futures. We situate the EJIT principles within the broader landscape of environmental justice, design justice, and post-growth computing, proposing them as a values infrastructure for resisting extractive defaults and envisioning technological systems that operate in reciprocity with people and the planet. In doing so, this article aims to support collective efforts to transform not only what technologies we build, but how, why, and for whom.
The rise of generative artificial intelligence (GAI) has led to alarming predictions about its environmental impact. However, these predictions often overlook the fact that the diffusion of innovation is accompanied by the evolution of products and the optimization of their performance, primarily for economic reasons. This can also reduce their environmental impact. By analyzing the GAI ecosystem using the classic A-U innovation diffusion model, we can forecast this industry's structure and how its environmental impact will evolve. While GAI will never be green, its impact may not be as problematic as is sometimes claimed. However, this depends on which business model becomes dominant.
This study explores the intersection of technological innovation and environmental sustainability in the context of Bitcoin mining. With Bitcoin's growing adoption, concerns surrounding the energy consumption and environmental impact of mining activities have intensified. The study examines the core process of Bitcoin mining, focusing on its energy-intensive proof-of-work mechanism, and provides a detailed analysis of its ecological footprint, especially in terms of carbon emissions and electronic waste. Various models estimate that Bitcoin's energy consumption rivals that of entire nations, highlighting serious sustainability concerns. To address these issues, the paper unearths potential technological innovations, such as energy-efficient mining hardware and the integration of renewable energy sources, as viable strategies to reduce environmental impact. Additionally, the study reviews current sustainability initiatives, including efforts to lower carbon footprints and manage electronic waste effectively. Regulatory developments and market-based approaches are also discussed as possible pathways to mitigate the environmental harm associated with Bitcoin mining. Ultimately, the pa
To explore the relationship between corporate green technological innovation and the risk of stock price crashes, we first analyzed the data of listed companies in China from 2008 to 2018 and constructed indicators for the quantity and quality of corporate green technology innovation. The study found that the quantity of green technology innovation is not related to the risk of stock price crashes, while the quality of green technology innovation is negatively related to the risk of stock price crashes. Second, we studied the impact of corporate ownership on the relationship between the quality of green technological innovation and the risk of stock price crashes and found that in nonstate-owned enterprises, the quality of green technological innovation is negatively correlated with the risk of a stock price collapse, while in state-owned enterprises, the quality of green technological innovation and the risk of a stock price collapse are positive and not significant. Furthermore, we studied the mediating effect of the number of negative news reports in the media of listed companies on the relationship between the quality of corporate green technology innovation and the stock price
Innovation and technology management is an inevitable issue in the high end technological and innovative organizations. Today, most of the innovations are limited with developed countries like USA, Japan and Europe while developing countries are still behind in the field of innovation and management of technology. But it is also becoming a subject for rapid progress and development in developing countries. Innovation and technology environment in developing countries are by nature, problematic, characterized by poor business models, political instability and governance conditions, low education level and lack of world-class research universities, an underdeveloped and mediocre physical infrastructure, and lack of solid technology based on trained human resources. This paper provides a theoretical and conceptual framework analysis for managing innovation and technology in developing countries like India and China. We present the issues and challenges in innovation and technology management and come up with proposed solutions.
With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.
Australia is seen as lagging in the innovation that is needed for corporate success and national productivity gains. There is an apparent lack of consistent and integrated advice to managers on how to undertake innovation. Thus, this study aims to develop and investigate a framework that relates innovation practices to the type of innovation outcome, in the context of Information Technology (IT) enabled innovations. An Innovation Practice Framework was developed based on the Knowledge-Innovation Matrix (KIM) proposed by Gregor and Hevner (2015). Eleven commonly used innovation techniques (practices) were identified and placed in one or more of the quadrants: invention, advancement, exaptation and exploitation. Interviews were conducted with key informants in nine organisations in the Australian Capital Territory. Results showed that the least used techniques were skunk works and crowdsourcing. The most used techniques were traditional market research, brainstorming and design thinking. The Innovation Practice Framework was given some support, with genius grants being related to invention outcomes, design thinking with exaptation, traditional R&D with advancement and managerial
Using a firm-level dataset from the Spanish Technological Innovation Panel (2003-2016), this study explores the characteristics of environmentally innovative firms and quantifies the effects of pursuing different types of environmental innovation strategies (resource-saving, pollution-reducing, and regulation-driven innovations) on sales, employment, and productivity dynamics.
Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of design innovation and thus make breakthroughs. In this work, we construct a technology fitness landscape via deep neural embeddings of patent data. The landscape consists of 1,757 technology domains and their respective improvement rates. In the landscape, we found a high hill related to information and communication technologies (ICT) and a vast low plain of the remaining domains. The landscape presents a bird's eye view of the structure of the total technology space, providing a new way for innovators to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation.
This study investigates the interconnectivity of firms and Environmental Justice Organizations (EJOs) involved in socio-environmental conflicts worldwide, using data from the Environmental Justice Atlas (EJAtlas). By constructing a multilayer network that links firms, conflicts, and EJOs, the research applies social network analysis to evaluate the simultaneous involvement of these actors across multiple disputes. Both projected networks of firms and EJOs have been analysed by aggregating nodes by categories and countries to reveal structural differences. Findings reveal a stark contrast between the interconnectedness of firms and EJOs. Multinational corporations form a cohesive global network, enabling them to coordinate strategies and exert influence across regions. Conversely, EJOs are fragmented, often operating in isolated clusters with limited interconnection but forming a robust, decentralized and self-organized global network. Firms network present a strong dependence on pertaining conflict category while EJOs network does not depend on conflict category. This structural difference suggests a risk of systemic and structural coordination for firms towards exploitative expans
Artificial Intelligence (AI) is changing the world, but its impacts on the environment and human well-being remain uncertain. We conducted a systematic literature review of 1,291 studies selected from 6,655 records, identifying the main impacts of AI and how they are assessed. The evidence reveals an uneven landscape: 72% of environmental studies focus narrowly on energy use and CO2 emissions, while only 11% consider systemic effects. Well-being research is largely conceptual and overlooks subjective dimensions. Strikingly, 83% of environmental studies portray AI's impacts as positive, while well-being analyses show a near-even split overall (44% positive; 46% negative). However, this split masks differences across well-being dimensions. While the impacts of AI on income and health are expected to be positive, its impacts on inequality, social cohesion, and employment are expected to be negative. Based on our findings, we suggest several areas for future research. Environmental assessments should incorporate water, material, and biodiversity impacts, and apply a full life-cycle perspective, while well-being research should prioritise empirical analyses. Evaluating AI's overall impa
In this paper, we present our champion solution to the Global Artificial Intelligence Technology Innovation Competition Track 1: Medical Imaging Diagnosis Report Generation. We select CPT-BASE as our base model for the text generation task. During the pre-training stage, we delete the mask language modeling task of CPT-BASE and instead reconstruct the vocabulary, adopting a span mask strategy and gradually increasing the number of masking ratios to perform the denoising auto-encoder pre-training task. In the fine-tuning stage, we design iterative retrieval augmentation and noise-aware similarity bucket prompt strategies. The retrieval augmentation constructs a mini-knowledge base, enriching the input information of the model, while the similarity bucket further perceives the noise information within the mini-knowledge base, guiding the model to generate higher-quality diagnostic reports based on the similarity prompts. Surprisingly, our single model has achieved a score of 2.321 on leaderboard A, and the multiple model fusion scores are 2.362 and 2.320 on the A and B leaderboards respectively, securing first place in the rankings.
The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.
Technological innovation is an important aspect of teaching and learning in the 21st century. This article examines faculty attitudes toward technology use in the classroom at one regional public university in the United States. Building on a faculty-led initiative to develop a Community of Practice for improving education, this study used a mixed-method approach of a faculty-developed, electronic survey to assess this topic. Findings from 72 faculty members revealed an overall positive stance toward technology in the classroom and the average faculty member utilized about six technology tools in their courses. The opportunities, barriers and future uses for technologies in the higher education classroom emerged from the open-ended questions on the survey. One finding of particular concern is that faculty are fearful that technology causes a loss of the humanistic perspective in education. The university is redesigning ten of its most popular courses to increase flexibility, accessibility and student success.
In an age of fast-paced technological change, patents have evolved into not only legal mechanisms of intellectual property, but also structured storage containers of knowledge full of metadata, categories, and formal innovation. This chapter proposes to reframe patents in the context of information science, by focusing on patents as knowledge artifacts, and by seeing patents as fundamentally tied to the global movement of scientific and technological knowledge. With a focus on three areas, the inventions of AIs, biotech patents, and international competition with patents, this work considers how new technologies are challenging traditional notions of inventorship, access, and moral accountability.The chapter provides a critical analysis of AI's implications for patent authorship and prior art searches, ownership issues arising from proprietary claims in biotechnology to ethical dilemmas, and the problem of using patents for strategic advantage in a global context of innovation competition. In this analysis, the chapter identified the importance of organizing information, creating metadata standards about originality, implementing retrieval systems to access previous works, and ethi
In order to break the limitation of plasma nitriding technology,which can be applied to a few nonmetallic gaseous elements, the "Double Glow Discharge Phenomenon" was found and then invented the "Double Glow Plasma Surface Metallurgy Technology". This double glow plasma surface metallurgy technology can use any element in the periodic table of chemical elements for surface alloying of metal materials. Countless surface alloys with special physical and chemical properties have been produced on the surfaces of conductive materials.By using double glow discharge phenomenon,a series of new plasma technologies,such as the double glow plasma graphene technology, double glow plasma brazing technology,double glow plasma sintering technology, double glow plasma nanotechnology,double glow plasma cleaning technology, double glow plasma carburizing without hydrogen and so on, have been invented.A very simple phenomenon of double glow discharge can generate about 10 plasma innovation technologies, which fully shows that there is still a lot of innovation space on the basis of classical physics.This paper briefly introduces the basic principles,functions and characteristics of each technology. T
Technological knowledge evolves not only through the generation of new ideas, but also through the reinterpretation of existing ones. Reinterpretations lead to changes in the classification of knowledge, that is, reclassification. This study investigates how reclassified inventions can serve as renewed sources of innovation, thereby accelerating technological progress. Drawing on patent data as a proxy for technological knowledge, I discuss two empirical patterns: (i) more recent patents are more likely to get reclassified and (ii) larger technological classes acquire proportionally more reclassified patents. Using these patterns, I develop a model that explains how reclassified inventions contribute to faster innovation. The predictions of the model are supported across all major technology domains, suggesting a strong link between reclassification and the pace of technological advancement. More generally, the model connects various, seemingly unrelated knowledge quantities, providing a basis for knowledge intrinsic explanations of growth patterns.