In rapidly urbanizing regions, designing climate-responsive urban forms is crucial for sustainable development, especially in dry arid-climates where urban morphology has a significant impact on energy consumption and environmental performance. This study advances urban morphology evaluation by combining Urban Building Energy Modeling (UBEM) with machine learning methods (ML) and Explainable AI techniques, specifically Shapley Additive Explanations (SHAP). Using Tehran's dense urban landscape as a case study, this research assesses and ranks the impact of 30 morphology parameters at the urban block level on key energy metrics (cooling, heating, and lighting demand) and environmental performance (sunlight exposure, photovoltaic generation, and Sky View Factor). Among seven ML algorithms evaluated, the XGBoost model was the most effective predictor, achieving high accuracy (R2: 0.92) and a training time of 3.64 seconds. Findings reveal that building shape, window-to-wall ratio, and commercial ratio are the most critical parameters affecting energy efficiency, while the heights and distances of neighboring buildings strongly influence cooling demand and solar access. By evaluating urban blocks with varied densities and configurations, this study offers generalizable insights applicable to other dry-arid regions. Moreover, the integration of UBEM and Explainable AI offers a scalable, data-driven framework for developing climate-responsive urban designs adaptable to high-density environments worldwide.

There has been a growing realisation among businesses in recent years that becoming environmentally sustainable is a must, not a choice. Customers, investors and employees and industry regulators are all putting pressure on them to act before the climate crisis worsens to calamitous levels. Alongside this, the willingness of companies to publicise their progress in reducing their ecological impact is increasing. More than 3,400 organisations, with a combined market cap of £21.4tn, have registered their support for the Task Force on Climate-Related Financial Disclosures since it published its first reporting recommendations in 2017, for instance. AI has a key role to play in helping firms to hit the ambitious net-zero CO2 emissions targets they are setting themselves. The Global AI Adoption Index 2022, IBM's latest annual survey of uptake, found that two-thirds of the 7,500 IT chiefs it polled were either using AI to achieve sustainability goals or planning to do so.

Autonomous bicycles have recently been proposed as a new and more efficient approach to bicycle-sharing systems (BSS), but the corresponding environmental implications remain unresearched. Conducting environmental impact assessments at an early technological stage is critical to influencing the design and, ultimately, environmental impacts of a system. Consequently, this paper aims to assess the environmental impact of autonomous shared bikes compared with current station-based and dockless systems under different sets of modeling hypotheses and mode-shift scenarios. The results indicate that autonomy could reduce the environmental impact per passenger kilometer traveled of current station-based and dockless BSS by 33.1 % and 58.0 %. The sensitivity analysis shows that the environmental impact of autonomous shared bicycles will mainly depend on vehicle usage rates and the need for infrastructure. Finally, this study highlights the importance of targeting the mode replacement from more polluting modes, especially as traditional mobility modes decarbonize and become more efficient.

A robotic solution that brings efficiency, flexibility and improved environmental performance to custom spray jobs on cars has been recognised with an award from IFR and IEEE. ABB's PixelPaint robotic non-overspray technology for the automotive industry has won this year's Innovation and Entrepreneurship in Robotics & Automation (IERA) Award for Outstanding Achievements in Commercialising Innovative Robot and Automation Technology. PixelPaint uses inkjet head technology to directly apply high resolution two-tone or individualised designs to a car body in a single pass, enabling manufacturers to meet the rising demand for customised paint jobs while eliminating overspray. "With PixelPaint, 100 per cent of the paint can now be applied in half the time compared to the previous method used for custom paint jobs, with a much better finish quality," said Joerg Reger, managing director of ABB Robotics Auto OEM Business Line. "For our customers, this provides the triple bonus of saving millions of dollars per year through reduced paint consumption, improved efficiency and improved environmental performance through reduced VOC and CO2 emissions, while meeting their customer needs."

Washington, DC, February 27, 2018 – InSite, a leading business intelligence solution that enables better operational, financial, and environmental performance, is pleased to welcome Brian Snow as a strategic advisor, assisting the executive leadership team on technology strategy and growth. Brian is a General Partner in Impala Ventures, a venture capital and advisory firm focused on the disruptive commercial real estate technology sectors. Brian has written extensively on trends that are leading the digitization of facilities management and real estate and the advancements in artificial intelligence and machine learning. "Brian brings a wealth of industry knowledge and advisory experience, particularly in the digitization of facilities and real estate. We are excited to have him help us as we consider the rapidly developing market," said Davor Kapelina, InSite President and Founder.