Sustainability

Examples include the use of green roofs, vertical gardens, and stormwater management systems like Singapore's Marina Barrage. Additionally, cities like Houston have implemented green spaces and permeable pavements to mitigate flood risks while enhancing environmental stewardship.

Cities can balance these costs by adopting a long-term perspective, such as life cycle costing, which considers the overall savings in maintenance, operational efficiency, and disaster recovery over time. This approach helps stakeholders appreciate the long-term benefits despite initial expenses.

AI contributes to sustainable manufacturing by optimizing production processes, reducing waste, and improving resource efficiency. It can analyze emissions data, optimize material usage, and enable real-time monitoring of production lines to minimize environmental impact[2][3][4].

Adopting sustainable manufacturing practices with AI can lead to significant financial benefits, including cost reductions through energy management and material waste reduction. AI can also help optimize production to meet demand more accurately, reducing overproduction and associated costs[2][5].

The new cloud trilemma involves balancing **edge computing** (for speed and efficiency), **sovereignty** (regarding data control and compliance), and **sustainability** (focusing on environmental impact). Effective management of these components is crucial for strategic maturity and ethical leadership.

Balancing operational efficiency with sustainability and regulatory adherence is important because it ensures that companies can maintain strategic maturity and ethical leadership. This balance helps companies navigate the complex demands of modern business, including environmental concerns and legal compliance, while maintaining operational effectiveness.

3D printing contributes to sustainability by reducing material waste and energy consumption compared to traditional manufacturing methods. It allows for the use of recycled materials and localized production, which can lower carbon emissions and minimize environmental impact.

Despite its benefits, 3D printing raises environmental concerns such as resource depletion, waste generation, and pollution. The production of plastic filaments involves raw material extraction, energy consumption, and emissions of volatile organic compounds (VOCs) during the printing process.

Simplifying product design can significantly reduce environmental impact by using fewer materials, reducing complexity, and enhancing recyclability. This approach limits the extraction and processing of raw materials, which are major contributors to greenhouse gas emissions and biodiversity loss[1][2].

Adopting sustainable product design can lead to substantial economic benefits, including streamlined assembly processes, reduced production costs, and increased product durability. These factors contribute to a more profitable business model while promoting sustainability[1][3].

AI systems require substantial computational power, leading to high energy consumption and significant carbon footprints, especially in data centers that support large AI models. Additionally, AI operations consume large amounts of water and generate electronic waste. These environmental costs mean that while AI can drive sustainability, its deployment must be managed carefully to minimize negative impacts, such as by optimizing energy efficiency, using renewable energy, and deploying AI on edge devices which are more energy-efficient.

Organizations should start with actionable steps such as adopting standardized procedures to measure AI's environmental impact, improving the energy efficiency of AI algorithms, and using renewable energy sources for data centers. They should also ensure human oversight to validate AI outputs, address data bias to avoid skewed sustainability insights, and implement strong cybersecurity measures to protect sensitive sustainability data. A holistic approach considering compute operations, electricity usage, carbon footprint, and water consumption is essential to maximize AI's potential in promoting eco-friendly practices.

Behavioural science plays a crucial role in encouraging sustainable tourism practices by identifying cognitive biases and barriers that influence tourists' decisions. It offers tools like default choice-setting, modifying choice architecture, and framing to make sustainable options more appealing and accessible. Additionally, it helps in designing human-centred and sustainable tourism services by understanding tourists' needs and expectations.

Tourists often struggle to maintain sustainable behaviors during vacations due to cognitive biases such as present bias and the intention-action gap. Additionally, social norms and the desire for pleasure during vacations can lead to less eco-friendly behaviors compared to those at home.

Lean manufacturing reduces environmental impact by systematically eliminating waste (overproduction, defects, excess inventory) and optimizing resource use through tools like Value Stream Mapping, which identifies energy-intensive processes and material inefficiencies. This minimizes raw material consumption, energy use, and carbon emissions while maintaining production efficiency.

Lean principles bridge operational efficiency and environmental stewardship by creating a culture of continuous improvement (Kaizen) that simultaneously addresses production waste and ecological impacts. Many companies focus solely on end-product sustainability without addressing process inefficiencies, making lean methodologies critical for reducing hidden waste streams in manufacturing.

Companies are retreating from sustainability commitments due to political pressure, economic uncertainty, and lack of progress toward ambitious targets. For example, Wells Fargo dropped its 2050 net-zero goal, while Coca-Cola reduced recycled content targets and delayed refillable bottle initiatives. These rollbacks often occur when goals are perceived as disconnected from core business strategy or financial performance.

Companies must align sustainability efforts with core business objectives, ensure financial materiality, and demonstrate measurable progress. Superficial commitments without clear operational integration invite skepticism. Leaders should focus on climate resilience as business risk management, as seen in investor demands for adaptation strategies following extreme weather events.

A circular economy for smartphones is a sustainable model that moves away from the traditional linear 'take-make-dispose' approach. Instead, it focuses on repairing, reusing, refurbishing, and recycling devices to reduce waste, conserve natural resources, and minimize environmental impact. This approach extends the lifecycle of smartphones by managing returns, recovering valuable materials, and reintroducing refurbished devices into the market.

Strategic partnerships in the smartphone industry facilitate collaboration across manufacturers, recyclers, and service providers to implement circular economy practices effectively. These partnerships enable efficient collection of used devices, refurbishment, recycling of valuable materials, and development of modular designs that simplify repairs. Such collaboration reduces electronic waste, lowers carbon emissions, and creates new business opportunities while improving profitability and environmental outcomes.

The circular economy is an economic model focused on designing out waste and pollution, keeping products and materials in use for as long as possible, and regenerating natural systems. Unlike the traditional linear economy, which follows a 'take-make-dispose' pattern, the circular economy aims to create closed-loop systems where resources are reused, repaired, refurbished, and recycled to minimize environmental impact and resource consumption.

AI supports the circular economy by optimizing resource efficiency and waste reduction through predictive maintenance, improving waste sorting with computer vision, and enhancing supply chain transparency and demand forecasting. It also aids in designing products for circularity by enabling better material selection, modularity, and lifecycle tracking via digital twins or material passports. Additionally, AI optimizes reverse logistics for efficient collection and refurbishment of used products, thereby reducing waste and improving sustainability.