Tracking AI in climate innovation

Innovation in artificial intelligence (AI) is spreading rapidly in many areas of technology, and AI technologies may be of help to mitigate and adapt to climate change. However, previous studies of AI in the climate context mainly rely on expert judgement of the research literature, not large-scale data. Here, we present a new approach to analyzing the relation between AI and climate innovation on the economy-wide scale. We analyze over six million patents from the past 45 years from the United States, and find that the greatest amount of climate AI innovation has occured in transportation, energy, and manufacturing technologies. Green ICT and climate adaptation technologies is where AI innovations have higher shares, and breakthrough innovations have made up a larger share in adaptation technologies compared to technologies for climate mitigation. We estimate the difference that AI makes with statistical analysis: AI in mitigation and adaptation technologies is associated with 30-100% more subsequent innovations. Our approach provides new capabilities to track the exponential growth of AI in climate innovation.

Simulation Methods for the Analysis of Complex Systems

Everyday systems like communication, transportation, energy and industrial systems are an indispensable part of our daily lives. Several methods have been developed for their reliability assessment—while analytical methods are computationally more efficient and often yield exact solutions, they are unable to account for the structural and functional complexities of these systems. These complexities often require the analyst to make unrealistic assumptions, sometimes at the expense of accuracy. Simulation-based methods, on the other hand, can account for these realistic operational attributes but are computationally intensive and usually system-specific. This chapter introduces two novel simulation methods: load flow simulation and survival signature simulation which together address the limitations of the existing analytical and simulation methods for the reliability analysis of large systems.

Do Tourism Development, Energy Consumption and Transportation Demolish Sustainable Environments? Evidence from Chinese Provinces

China is performing a dominant role in the world’s economic growth, but it has mainly been the commencement of the Belt and Road Initiative (BRI) that has significantly increased its importance around the world. Recently, the emergence of the tourism industry has been considered as an alternative for sustainable economic and ecological development, which is ironic. Although China is promoting tourism in various regions under “The New Normal” phase, it needs to proactively address the challenges of dismantling, for the environment. The fundamental objective of the current study is to determine the long-term affiliation between tourism development, economic progress, transportation, energy consumption, value added hotel catering services, and environmental degradation (CO2) for a panel of thirty (30) provinces of China over the period of 1995–2017. Primarily, we applied the CD test for investigating cross dependence; subsequently, conventional and CD based panel unit root tests (CIPS) were carried out to deal with the puzzle of the stationarity of the panel series. The results of the dynamics panel, DOLS, FMOLS, and PMG, indicated that transportation, energy consumption, and value added hotel and catering services have a strong positive association with carbon emission, but tourism development has mixed links with ecological degradation. Additionally, the causative based test revealed the bidirectional association of tourism development, transportation, economic progress, and energy consumption with environmental quality. The retrieved estimates conferred a few guidelines, concerning the presence of BRI projects, for the Chinese administration at the provincial and national level: initiating the renewable based energy projects and possibly wishing to decrease the use of fossil fuel based energy in the industry, transportation, and hotels and catering sectors. Furthermore, the prevalence of green investment in provinces may motivate economic progress and tourism development, without worsening the atmosphere.

GIS-based approach for evaluating a community intrinsic resilience index

Community resilience refers to the degree to which a community can survive and recover following a disaster. While resilience itself is well understood, decisions that would enhance resilience are interdependent and involve various stakeholders. There are indices for evaluating community resilience, but these have the shortcoming that they compare between political entities, such as counties. Therefore, one cannot ascertain that a county is truly resilient. In addition, natural disasters depend on the landscape and thus have no relation to the political boundaries. Our metric aims to capture the information into a Community Intrinsic Resilience Index (CIRI), which embodies the resilience level of four critical sectors: transportation, energy, health and socio-economic. As a case study, we computed CIRI for the counties within New Jersey. Results showed that within NJ, CIRI ranged from 63 to 80%. A post-disaster CIRI, following a scenario of flooding, revealed that two coastal counties would have low CIRI values due to the reduction in the road area and/or the GDP (local economy shut down) to below minimum values. We believe that our platform would further advance the efforts to fill the gap between resilience research and applications and would help decision and policy makers to integrate resilience within the planning and design phases of disaster management.

Process Improvement of Waste Collection, Materials Recycling And Sustainable Manufacturing

With the advancement of technologies and sustainability awareness, sustainable manufacturing has formed a trend and transformation in manufacturing industry is becoming inevitable. In order to cope with the manufacturing transformation, this study proposes a collection-recycling-manufacturing (CRM) model to envision the process simulation as well as the process improvements. In this transformation, reduction of materials, cost, transportation and energy, and elimination of CO2 emission are the objectives, and innovation is the key to drive the solutions into a concrete foundation. By applying simulation techniques in the optimization of recycling facility management, this study produces generic formula in Materials Recycling Facilities (MRF) topology design and transportation distance calculation. The formula is expected to predict operation through an optimization of MRF counts at the cost of transportation, energy consumption, and CO2 emission. This study also proposes solutions to fill-in the gap of Additive Manufacturing (AM) before becoming the industry mainstream. This study suggests an expansion of materials recycling coverage, and take full advantages of AM to penetrate market. Meanwhile, it identifies AM limitations supported by enhancement plan to streamline the transformation and to support sustainable manufacturing.