This statistic shows the estimated impact the use of artificial intelligence (AI) on environmental applications may have on the regional gross domestic products (GDP) in 2030. It is projected that Europe would be the region whose economy could benefit the most from using AI for environmental applications, increasing its GDP potentially by 5.4 percent by 2030, in comparison to a business-as-usual scenario. Globally, economic gain from using AI in the agriculture, energy, transport and water sector could reach between 3.6 to 5.2 trillion U.S. dollars by 2030.

This statistic shows the estimated impact the use of artificial intelligence (AI) on environmental applications may have on the net employment in 2030, by skill level. It is projected that technicians and associate professionals would witness the most job gains from using AI for environmental applications, potentially increasing its workforce by 3.8 percent by 2030, in comparison to a baseline scenario.

This statistic shows artificial intelligence's (AI) impact on greenhouse gas (GHG) emissions worldwide in 2030, by region. Used for environmental applications, AI is predicted to reduce GHGs in North America by 6.1 percent by 2030 in comparison to a business-as-usual scenario.

This statistic shows the estimated impact that the use of artificial intelligence (AI) on environmental applications may have on the regional net employment in 2030. It is projected that East Asia would be the region that could witness the most job gains from using AI for environmental applications, potentially increasing its workforce by 2.5 percent by 2030, in comparison to a baseline scenario. This outcome would be the equivalent to around 25.1 million added jobs.

Freight Facts and Figures - Freight Transportation Energy Use and Environmental Impacts

Organizations in 2022 are mostly focused on improving the organizations physical impact on environment when using artificial intelligence (AI). It is highly likely that this is due to such improved efficiency translating most easily to improved company numbers of growth and expenditure. Sourcing ethical products takes the least back seat among organizations as it can often have direct cost increases to the production and supply lines.

The dataset includes the LCA, LCIA, LCC, sensitivity analysis for the wastewater treatment expansion for co-digestion with food waste. This dataset is associated with the following publication: Morelli, B., S. Cashman, X. Ma, J. Turgeon, S. Arden, and J. Garland. Environmental and Cost Benefits of Co-Digesting Food Waste at Wastewater Treatment Plants. WATER SCIENCE AND TECHNOLOGY. IWA Publishing, London, UK, 82(2): 227-241, (2020).

LCA/LCCA/LCIA data used to create figures and tables in the papers.

This dataset is associated with the following publications: Ghimire, S., and J. Johnston. Holistic impact assessment and cost savings of rainwater harvesting at the watershed scale. Elementa: Science of the Anthropocene. University of California Press (UC Press), Oakland, CA, USA, 5(9): 1-17, (2017). Ghimire, S., and J. Johnston. A modified eco-efficiency framework and methodology for advancing the state of practice of sustainability analysis as applied to green infrastructure. Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, USA, 13(5): 821-831, (2017). Ghimire, S., J. Johnston, W. Ingwersen, and S. Sojka. Life cycle assessment of a commercial rainwater harvesting system compared with a municipal water supply system. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 151: 74–86, (2017).

The University of Michigan School for Environment and Sustainability (SEAS) is dedicated to transforming research into action to create a healthier planet for all. At SEAS, students, faculty, and partners work together to dig deeper into specific areas of impact and interest, known as Sustainability Themes. These themes cut across all specializations and give students and faculty a chance to explore and collaborate on projects that address the most pressing environmental challenges.

From academic rigor to real-world impact, SEAS is committed to producing game-changers who make a difference. With a range of graduate programs, including Master's and PhD degrees, students can explore their passions and develop their potential. The school's faculty and students are also dedicated to community engagement, with projects and initiatives that address environmental justice, sustainability, and conservation.

This shapefile contains a polygon representation of the USGS-BLM Coal Bed Methane Project Area, new environmental impact statement area in the Powder River Basin. This theme was created specifically for the National Coal Resource Assessment in the Northern Rocky Mountains and Great Plains Region.

The Information Resources Inc. is a well-established company that specializes in providing a wide range of data related to environmental regulations and policies. As a leading provider of environmental data, the company's primary focus is on collecting and disseminating information related to climate change, emissions, and sustainability. With a vast repository of data, the company's website offers a treasure trove of information on environmental laws, regulations, and standards, making it an invaluable resource for researchers, policymakers, and businesses alike.

From data on greenhouse gas emissions to information on environmental impact assessments, Information Resources Inc. has a vast array of data sets that can be leveraged for research, analysis, and decision-making. With a strong emphasis on quality and accuracy, the company's data is meticulously curated and updated regularly to reflect the latest developments in the industry. Whether you're looking for insights on environmental policy or data on sustainable technologies, Information Resources Inc. is an indispensable resource for anyone working in the environmental sector.

The Environmental Intelligence Suite market is experiencing robust growth, driven by increasing concerns about climate change, stricter environmental regulations, and the rising need for sustainable business practices. The market, estimated at $5 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The adoption of cloud-based solutions is accelerating, offering scalability and cost-effectiveness to businesses of all sizes. Furthermore, the demand for real-time environmental data is surging across various sectors, including automotive, food and beverage, manufacturing, energy and utilities, and healthcare. These industries are leveraging environmental intelligence to optimize operations, reduce environmental impact, and enhance risk management. The integration of AI and machine learning is further enhancing the capabilities of these suites, enabling more accurate predictions and proactive mitigation strategies. However, the market's growth is not without challenges. High initial investment costs, a lack of standardization across platforms, and the need for skilled professionals to interpret and utilize complex environmental data are some of the key restraints. Despite these hurdles, the long-term prospects for the Environmental Intelligence Suite market remain positive. The increasing availability of affordable sensors, the growing adoption of IoT technologies, and government initiatives promoting sustainability are all expected to contribute to market expansion in the coming years. Key players like BreezoMeter, IBM, Cerensa, and Ecochain Technologies are actively shaping the market landscape through continuous innovation and strategic partnerships. Geographical expansion, particularly in developing economies, is also anticipated to drive substantial growth. The market is segmented by deployment (cloud-based and on-premise) and application across various industries, providing significant opportunities for specialized solutions and targeted market penetration.

[This dataset contains all data used for Studies 2 (qualitative), 3 (quantitative survey) and 4 (longitudinal) in my PhD research.]

Thesis abstract:

This thesis explores the potential positive impact of artificial intelligence (AI) technology on sustainability in and outside of the tourism industry through four studies. Study 1 introduced the AI4GoodTourism framework, emphasising the need for sustainability inclusion and tourist involvement to achieve a successful sustainability transition. Five themes were identified through a systematic review: intelligent automation to enhance tourist experience, preserve heritage, promote quality of life, measure tourist experience, and preserve the environment. The latter theme was the least explored scholarly topic. Study 2 conceptualised a conversational AI chatbot to promote pro-environmental behaviour spillover among tourists visiting the Gili Islands, Indonesia. A theoretical model was proposed, highlighting factors influencing chatbot usage and spillover effects. Study 3 identified relationships between factors from Study 2, revealing that factors such as performance expectancy, timing, and credibility significantly influenced people’s intention to use the proposed chatbot technology. A significant relationship was established between people’s intentions to use the chatbot and environmentally friendly transport. Scenario-based experiments showed that using the chatbot with educational information on sustainability was sufficient to trigger behaviour change. Study 4 explored the underlying mechanism of pro-environmental behaviour spillover through human-chatbot interactions using flashback nudging. A longitudinal experiment involving the Gili tourists demonstrated that flashback nudging delivered through chatbot technology strengthened their environmental self-identity, leading to significant differences in self-reported pro-environmental behaviour between treatment and control groups. In conclusion, the thesis demonstrates that AI technology, designed with high sustainability inclusion, can positively impact sustainability through tourists’ marginal contributions. The proposed AI4GoodTourism framework and the conceptualised chatbot technology, especially with flashback nudging, show potential for facilitating pro-environmental behaviour spillovers among tourists. All four studies in this thesis highlight the importance of prioritising sustainability in AI innovations for the tourism industry, offering insights for future AI development and adoption to support the global sustainability agenda.

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Analysis of ‘EIA Location Point’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-housinggovie-opendata-arcgis-com-datasets-e1b23ea1375a47cdb689389a18888629_0 on 11 January 2022.

--- Dataset description provided by original source is as follows ---

This feature service is used to collect primary information on Environmental Impact Assessments and display the information on the EIA Web Aap. The ‘Environmental Impact Assessment Open Data Project’ is carried out by the GIS Department to compliment the EU Directive 2014/52/EU which is currently being transposed.

--- Original source retains full ownership of the source dataset ---

The AI Intelligent Visualization Gateway market is experiencing robust growth, driven by the increasing demand for advanced monitoring and control systems within power distribution networks. The convergence of artificial intelligence (AI) and visualization technologies offers significant improvements in operational efficiency, predictive maintenance, and grid stability. The market's expansion is fueled by the global shift towards smart grids, the rising adoption of renewable energy sources requiring sophisticated management, and the increasing need for real-time data analysis to prevent outages and optimize resource allocation. Key application segments, including medium and low voltage, high voltage, and ultra-high voltage distribution stations, are all contributing to market growth, with the high-voltage segment expected to lead in the near future due to its critical role in long-distance power transmission. Different gateway types, such as cloud and edge gateways, cater to varying needs for data processing and security, furthering market segmentation. While initial investment costs can be a restraint, the long-term benefits in terms of reduced operational expenses, improved grid reliability, and minimized environmental impact outweigh this challenge, ensuring continued market expansion. The competitive landscape features a mix of established players and emerging technology companies. Well-established players leverage their expertise in power systems and networking, while newer entrants bring innovation in AI algorithms and data visualization techniques. Geographic growth is widespread, with North America and Asia Pacific currently dominating the market due to substantial investments in infrastructure modernization and digital transformation. However, strong growth is anticipated in other regions, especially in developing economies experiencing rapid industrialization and urbanization, leading to increased power demand. The forecast period (2025-2033) suggests continued market expansion driven by technological advancements and rising government initiatives promoting smart grid development, implying significant opportunities for market participants focused on innovation and tailored solutions. Let's assume a conservative CAGR of 15% based on industry trends and the rapid adoption of smart grid technologies.

This dataset contains a list of products that carry the Design for the Environment (DfE) label. This mark enables consumers to quickly identify and choose products that can help protect the environment and are safer for families. When you see the DfE logo on a product it means that the DfE scientific review team has screened each ingredient for potential human health and environmental effects and that-based on currently available information, EPA predictive models, and expert judgment-the product contains only those ingredients that pose the least concern among chemicals in their class. Product manufacturers who become DfE partners, and earn the right to display the DfE logo on recognized products, have invested heavily in research, development and reformulation to ensure that their ingredients and finished product line up on the green end of the health and environmental spectrum while maintaining or improving product performance. EPA's Design for the Environment Program (DfE) has allowed use of their logo on over 2500 products. These products are formulated from the safest possible ingredients and have reduced the use of "chemicals of concern" by hundreds of millions of pounds. A Spanish version of this dataset is available for download at https://www.epa.gov/dfe/pubs/products/list_of_labeled_products.html

Climate change is an urgent issue that must be addressed by individuals, communities, governments, and organizations around the world. Its immediate effects include extreme weather events and unpredictable rainfall, impacting various sectors. Aligned with UN Sustainable Development Goals, addressing climate change is paramount. Despite deteriorating environmental conditions, technology continues to improve. It might be able to withstand and potentially even reduce the effects of climate change on the environment. Artificial intelligence is a technology that has been evolving quickly during the past years. Artificial intelligence is present in many aspects of our daily lives, such as search recommendations on social media. This systematic literature review examines 54 referenced papers, utilizing the Kitchenham approach to validate five research questions. According to the statistics, the Random Forest technique was employed in 18 out of 54 studies to build artificial intelligence for climate change issues. China emerged as the leader in conducting studies on AI's role in addressing climate change challenges. Within climate change research, hydrology stands out as a prominent and extensively discussed aspect. Overall, AI for Climate Change has made considerable improvements, highlighting the significance of continuing study in this specific area.

Cybersecurity remains the primary concern for organizations adopting artificial intelligence (AI) within their business in 2022. This is likely because the recent nature of AI along with a desire to keep enterprise interests secret means businesses keep a close eye on the risk of adopting new programs. Political stability and national security were the least of business concern, with barely nine and 13 percent of businesses considering them relevant. It is likely that these do not seem to be business concerns, as it should be the responsibility of government to monitor these security risks. National security did see a considerable growth in concern from 2019, likely owing to the increased securitisation of AI in governmental discourse.

The Ai Based Gas Analyzer Market is projected to grow from USD 2.1 billion in 2023 to USD 4.9 billion by 2033, at a CAGR of 8.82%. The market is driven by increasing environmental concerns, stringent government regulations, and growing demand for gas analysis in various industries such as chemical, oil and gas, and power generation. Ai Based Gas Analyzers offer real-time monitoring, improved accuracy, reduced maintenance costs, and enhanced safety. Key trends in the Ai Based Gas Analyzer Market include: 1) Advancements in sensor technology, with the development of more sensitive and selective sensors, enabling the detection of a wider range of gases at lower concentrations. 2) Integration of artificial intelligence (AI) and machine learning (ML) algorithms for data analysis and pattern recognition, allowing for more efficient and accurate gas measurements. 3) Growing adoption of wireless and cloud-based technologies, providing remote monitoring and data access. The major players in the Ai Based Gas Analyzer Market are KROHNE, Ametek, Emerson, Honeywell, Endress+Hauser, Process Insights, Yokogawa Electric, Agilent Technologies, Thermo Fisher Scientific, ABB, McCrometer, Parker Hannifin, Teledyne Technologies, Siemens, and Horiba. Recent developments include: Recent developments in the AI-Based Gas Analyzer Market indicate a significant focus on innovation and technology enhancement among key players such as KROHNE, Ametek, Emerson, Honeywell, Endress+Hauser, and others. The increasing demand for efficient gas analysis solutions, driven by stringent environmental regulations and the push for sustainable practices, continues to shape the market landscape., Noteworthy, companies like ABB, Siemens, and Teledyne Technologies have reported advancements in their AI-based technology, improving accuracy and response time for gas detection. Market growth is further fueled by recent strategic mergers and acquisitions, particularly among these companies, with Emerson acquiring a complementary technology firm to strengthen its market position., This consolidation trend is aimed at leveraging synergies, enhancing product offerings, and expanding market reach. The overall market valuation is witnessing an upward trajectory, reflecting increased investments and a growing interest in automated and AI-driven solutions. With the rise of smart manufacturing and Industry 4.0 practices, the role of AI-based gas analyzers is becoming crucial in industrial applications, ensuring compliance and operational efficiency while reducing environmental impact.. Key drivers for this market are: Rising demand for environmental monitoring, Integration with IoT technologies; Increased focus on industrial safety; Growth in the renewable energy sector; Advancements in machine learning algorithms. Potential restraints include: Growing demand for environmental monitoring, Increasing regulations on emissions; Advancements in AI technology; Rising demand in industrial applications; and Need for real-time data analysis..