When we talk about the energy transition, the conversation often centers on renewables, smart grids, and data platforms. But the real transformation doesn’t happen in control rooms or data centers; it happens through people.
Energy Transition is not only about Technology – It’s about People.
The grid of the future may be powered by electrons, but it will be shaped by humans. While digital platforms and renewables provide the tools, it is people who ensure those tools are used wisely, safely, and sustainably. The engineers who design it, the policymakers who regulate it, and the consumers who use it responsibly. And this transformation demands more than just technical skill. It calls for a cultural shift in how we think about energy. Collaboration across disciplines, continuous learning, and adaptive leadership are now as critical as engineering precision. Building the Human Grid means nurturing talent that can bridge the physical and digital worlds, translate complex data into meaningful decisions, and embed sustainability into everyday actions. Only when people, policy, and technology evolve together can the energy transition truly achieve its purpose.
Read the full article in my LinkedIn post where I have discussed further into this transformation and the next great challenge is not just deploying new technologies, but building the skills, governance frameworks, and behaviors that will empower people to drive the energy transition forward.
The global dependence on fossil fuels has been a key driver of industrial progress, but it has also led to unprecedented environmental, economic, and health challenges. Fossil fuels, including coal, oil, and natural gas, are the largest contributors to greenhouse gas emissions, which in turn exacerbate climate change and its devastating effects—rising temperatures, extreme weather events, and declining air quality.
As global energy demand continues to rise, the urgent need to transition to cleaner, renewable energy sources becomes critical. Renewable energy not only offers a path to mitigate the negative impacts of fossil fuels but also holds promise for more sustainable economic growth, job creation, and enhanced energy security. This blog delves into the top-down view of the challenges created by fossil fuels and why the shift to renewables is essential for a more sustainable future.
75% of global greenhouse gas emissions are from fossil fuels burning
Known challenges from fossil
Greenhouse Gas Emissions and Climate Change: Fossil fuels (coal, oil, and natural gas) are the largest contributors to greenhouse gas emissions, primarily carbon dioxide (CO2). These emissions trap heat in the Earth’s atmosphere, leading to global warming and extreme climate changes, such as rising sea levels, droughts, wildfires, and more frequent severe weather events. Impact: The burning of fossil fuels contributes to about 75% of global greenhouse gas emissions, making it a critical driver of climate change.
Resource Depletion and Energy Security: Fossil fuels are finite resources. As reserves diminish, there is growing concern over energy security and rising prices. Geopolitical tensions and supply chain disruptions can affect the availability of fossil fuels, leading to market volatility. Impact: Global reliance on fossil fuels creates vulnerabilities in energy supply chains, particularly for nations that import large amounts of energy.
Environmental Degradation: Extracting and processing fossil fuels cause widespread environmental damage, including deforestation, habitat destruction, water pollution from oil spills, and the destruction of ecosystems due to mining and drilling activities. Impact: The environmental costs of fossil fuel extraction and use have long-term effects on biodiversity and the health of ecosystems.
Public Health Issues: Fossil fuel combustion leads to air pollution, releasing harmful particulates, nitrogen oxides (NOx), and sulfur dioxide (SO2), which contribute to respiratory diseases, heart disease, and premature deaths. Impact: According to the World Health Organization (WHO), air pollution from fossil fuels causes approximately 7 million deaths annually .
The Critical Need for Alternate Energy Sources
Transitioning to renewable energy is no longer a choice but a necessity. It provides a solution to mitigate climate change, reduce energy dependence, ensure energy security, and foster sustainable economic growth. Governments and corporations worldwide are increasing investments in renewable energy to meet decarbonization targets and global climate agreements like the Paris Agreement.
The urgency to transition from fossil fuels to alternate energy sources is primarily driven by the environmental, economic, and social impacts of continued reliance on conventional energy. Fossil fuels, such as coal, oil, and natural gas, account for the majority of CO2 emissions, which significantly contribute to global climate change. As the planet warms, we face increasingly severe consequences such as rising sea levels, extreme weather events, and the loss of biodiversity.
By 2030, a global shift to renewable energy could save $4.2 trillion in costs related to climate change and public health impacts.
Why Transition to Renewable Energy is Urgent?
Environmental Benefits: The energy sector is responsible for approximately 73% of global greenhouse gas emissions. To limit global warming to 1.5°C as per the Paris Agreement, carbon emissions need to be cut by 45% by 2030 from 2010 levels. Renewable energy sources like solar, wind, and hydropower can drastically reduce CO2 emissions. For example, replacing coal with renewable energy could reduce emissions by 80-90% per unit of electricity generated energy.
Security and Independence: Renewable energy sources are abundant and decentralized, helping nations reduce their reliance on fossil fuel imports. As global oil and gas supplies face political and economic fluctuations, renewables offer a stable alternative that enhances energy security.
Economic Value: The global renewable energy market was valued at approximately $880 billion in 2020 and is expected to grow to $1 trillion by 2030, with a compound annual growth rate (CAGR) of 8.4%. This transition is a significant economic opportunity. Investment in renewable energy will create millions of jobs. For instance, the International Renewable Energy Agency (IRENA) estimates that 42 million jobs could be created in the renewable energy sector by 2050.
Technological Advancements: Advances in energy storage, smart grids, and energy management systems (EMS) are making renewable energy sources more reliable and scalable. Solar power prices have fallen by over 80% in the last decade, while wind power has seen a 40% cost reduction. These trends make renewables increasingly competitive with fossil fuels.
Health and Social Benefits: Fossil fuel pollution is linked to millions of premature deaths annually. According to the World Health Organization (WHO), air pollution is responsible for 7 million deaths globally each year. Transitioning to renewable energy can improve air quality and reduce health care costs associated with pollution.
In summary, moving to alternate energy sources is not only crucial for addressing climate change but also offers significant economic, social, and health benefits. Delaying this transition could result in irreversible environmental damage and economic losses. By acting now, countries and industries can position themselves for a sustainable, resilient future.
Most nations will have transitioned to 80-90% renewable energy, driven by solar, wind, and green hydrogen solutions by 2050
Promising Renewable Energy Sources
Solar Energy: Solar power harnesses energy from the sun using photovoltaic (PV) cells or solar thermal collectors.Solar energy capacity has grown exponentially. In 2022, global solar capacity reached 1 TW (terawatt) and is expected to double by 2030. Innovations in energy storage, solar panel efficiency, and government incentives have fueled rapid adoption.
Wind Energy: Wind energy uses turbines to convert kinetic energy from wind into electricity. Offshore and onshore wind energy have seen significant growth, particularly in Europe and Asia. The global wind energy capacity exceeded 800 GW in 2022, with major investments in offshore wind farms due to their higher efficiency and stronger wind conditions.
Hydropower: Hydropower converts the kinetic energy of flowing water into electricity. It is the most established renewable energy source, accounting for 16% of global electricity generation. Although mature, new hydropower projects are being designed to be more environmentally sustainable, and pumped storage hydropower (PSH) is gaining attention for its ability to store energy, balancing supply and demand.
Geothermal Energy: Geothermal power harnesses heat from the Earth’s core to generate electricity. While geothermal has the potential for base-load power generation, its expansion is limited to regions with significant geothermal activity, such as Iceland, the U.S., and parts of Asia.
Hydrogen Energy: Hydrogen can be used as a clean energy carrier. Green hydrogen, produced via electrolysis using renewable energy, is gaining traction.Green hydrogen has emerged as a key component of energy strategies for hard-to-decarbonize sectors, including heavy industry, shipping, and aviation. Major economies, including the EU and Japan, have committed to hydrogen roadmaps for large-scale production.
Conclusion
The global energy landscape is undergoing a transformation, driven by the urgent need to reduce dependence on fossil fuels. While fossil fuels have powered economies for over a century, they have come at a steep environmental and social cost. The shift to renewable energy is essential for addressing climate change, improving energy security, and fostering sustainable development.
The renewable energy sector is expanding rapidly, with solar, wind, hydropower, and emerging technologies like hydrogen leading the way. As these technologies become more cost-effective and scalable, they hold the key to a sustainable and cleaner energy future. Leadership in renewable energy will require a deep understanding of these technologies, their potential, and the evolving energy market dynamics.
References
Here are some reliable sources you can reference in your blog’s reference section, focusing on the timeline and quantified data points related to the transition to renewable energy:
According to the latest available data, there are approximately 17 billion connected Internet of Things (IoT) devices and this figure is expected to almost double to 29 billion by 2030. As per verified market research, IoT Devices Market size was valued at USD 125 B$ in 2023 and is projected to reach 620 B$ by 2031 (1).
The growing adoption of smart devices has driven demand for IoT devices, making sustainability more critical than ever as IoT’s role in business continues to expand. Most of the IoT devices have dangerous elements inside, ranging from heavy metals such as lead, mercury, cadmium and beryllium to hazardous chemicals like brominated flame retardants.
They are smaller in size!
The good news is; compared to other technology products, IoT devices has lesser environmental impact and they can deliver greener results. IoT devices are small physical devices, so their production requires fewer raw materials and lesser plastics and packaging materials, thus producing less e-waste.
The unique design of IoT devices has inherent restrictions in memory, so they use embedded systems programming which can run in such restricted conditions. IoT industry dictates the development of more complex systems that can be run on limited resources. IoT devices are compact and smaller than regular computers, designed for seamless integration into various environments. Despite their size, they can perform critical tasks, including real-time monitoring and data analysis. Their efficiency and versatility make them essential in smart systems and automation. Manufacturing of IoT devices requires less material as well as less energy than a large computers.
IoT devices are used primarily for data collection. They continuously collect information and send it to data centers for analysis. The whole process takes a lot of energy and is perhaps not always worth it. It would be a good practice to evaluate the real need for IoT use, in terms of what we gain and how much we spend in the race for popular digital instruments.
Where is the challenge?
The challenge is in it’s waste disposal, and associated environmental impact. Some of the critical concerns are:
Increased Volume of E-Waste: The proliferation of IoT devices leads to a significant increase in electronic waste, contributing to the growing global e-waste problem. 17B IoT devices as of today and it is growing exponentially.
Resource Consumption and Toxicity: IoT devices often contain rare earth metals and hazardous materials that are challenging to recycle and can be toxic to the environment if not disposed of properly.
Short Lifespan: Many IoT devices have short lifespans and are not designed for easy disassembly or recycling, leading to more frequent disposal and accumulation of waste in landfills.
As we discussed at the beginning, IoT devices often contain heavy metals such as lead, mercury, and cadmium. These metals can leach into soil and water, causing environmental contamination and health issues like neurological damage, kidney disease, and cancer. Lead exposure can cause high blood pressure and brain, kidney and reproductive health issues in adults. Exposure to mercury may cause irritation to the eyes, skin, and stomach, cough, chest pain, or difficulty breathing, insomnia, irritability, among others. IoT devices often contain lithium-ion batteries in them, which can pose fire hazards if damaged. Improper disposal can lead to toxic leaks, causing soil and water contamination.
What are sustainable approaches?
To reduce the environmental impact of IoT devices in terms of e-waste, several approaches can be considered:
Design for Longevity and Repairability: Develop IoT devices with longer lifespans, modular components, and easy repairability to reduce the need for frequent replacements.
Use of Sustainable Materials: Utilise recyclable and non-toxic materials in the manufacturing of IoT devices to minimise environmental harm.
Improved Recycling Programs: Establish and promote efficient e-waste recycling programs to ensure proper disposal and recovery of valuable materials from discarded IoT devices.
Standardisation and Interoperability: Encourage standardisation of components and interoperability between devices to reduce the need for multiple, redundant gadgets.
Manufacturer Take-Back Schemes: Implement take-back programs where manufacturers are responsible for collecting and recycling their products at the end of their lifecycle.
Consumer Awareness and Education: Educate consumers about the environmental impact of e-waste and encourage responsible purchasing, usage, and disposal of IoT devices.
Legislation and Regulation: Advocate for stricter regulations and policies that mandate environmentally friendly practices in the production, usage, and disposal of IoT devices.
Can GenAI come to the rescue?
Identifying alternative materials used in IoT devices is crucial as we use more and more connected devices and in-order-to address environmental concerns. The search for eco-friendly materials can reduce the environmental impact and promote the development of greener technologies. Generative AI (GenAI) can play a pivotal role in this process by analysing vast amounts of data to predict and discover new materials with desirable properties. GenAI can simulate the performance of these materials in various conditions, accelerating the research and development process. Additionally, GenAI can optimise manufacturing processes to incorporate these new materials efficiently, ensuring that IoT devices are not only sustainable but also cost-effective and high-performing.
Conclusion
The market demand for IoT devices is rapidly increasing as industries and consumers want to harness the power of connected technologies for enhanced efficiency, convenience, and innovation. However, this surge in demand brings about significant environmental challenges due to the materials and manufacturing processes currently used. Adopting sustainable approaches in the production and deployment of IoT devices is critical to mitigating these environmental impacts. Sustainable practices, such as using eco-friendly materials, improving energy efficiency, and implementing robust recycling programs, ensure that the growth of IoT does not come at the expense of our planet. By prioritising sustainability, manufacturers not only will meet regulatory requirements but also will contribute to a greener future while maintaining the technological advancements that IoT devices offer.
Generative AI is dominating conversations due to its transformative impact across industries. From generating creative content to aiding in problem-solving, these systems are revolutionizing how we approach tasks. With their ability to innovate, automate, and personalize, Generative AI technologies are at the forefront of shaping the future of technology and human interaction. Ensuring technology advancements benefit all, not just the privileged few, is paramount. While major tech players and governments strive for inclusive and equitable technology use, individuals must also acquire basic skills to prevent widening skill gaps. Accessible technology empowers everyone to participate fully in the digital age, fostering a more equitable and prosperous society.
Crafting well-designed prompts empowers users to steer the AI towards desired outcomes, maximizing creativity and relevance
Introduction
Rule based systems vs Trained systems
Trained systems learn from the data, they adapt and improve over time. Whereas, programmed systems follow predefined instructions without any learning capabilities. Today’s intelligent systems (using AI technology) are trained, not programmed. Instead of programming or writing specific rules to solve a problem, AI systems are fed with examples of ‘what it will encounter’ in the real world. AI systems then detect the patterns and produce their own ‘rules’ based on the examples. While trained systems offer flexibility and adaptation, programmed ones excel in precise execution.
So, Artificial Intelligence (AI) is the science and engineering of making intelligent machines, that can perform tasks which typically requires human intelligence (John McCarthy, 2007). At its simplest form, Artificial Intelligence is a field which combines Computer Science with robust data set.
AI systems typically perform functions like ‘classifying data’ (e.g. assigning labels to images), ‘grouping data’ (e.g. identifying customer segments with similar purchasing behavior), or ‘choosing actions’ (e.g. steering an autonomous vehicle).
What is Generative AI?
Generative AI (GenAI) is the latest development in AI field, where the primary function of the AI system is to generate content, content that is similar to/ indistinguishable from human-created content.
Generative AI is crucial because it empowers us to create new content, ranging from art to music to text, with minimal human intervention. Mastering generative AI allows for innovative problem-solving, creative expression, and automation across various fields, making it a valuable skill in today’s rapidly evolving technological landscape.
Who are the consumers?
Consumers of generative AI systems can be diverse, ranging from individual creators and artists to businesses and organizations across industries such as entertainment, marketing, design, education, healthcare, and more. These systems cater to anyone seeking automated content generation, personalized recommendations, enhanced creativity, or improved efficiency in their work processes. Additionally, consumers could include developers and researchers interested in advancing the capabilities of generative AI itself.
Why to embrace generative AI?
Understanding generative AI skills can be incredibly valuable for several reasons:
Creative Expression: Generative AI empowers individuals to express their creativity in new and exciting ways, whether it’s through generating art, music, literature, or even entirely new concepts. This skill allows for innovative exploration and pushes the boundaries of traditional creative processes.
Problem Solving: Generative AI techniques can be applied to various problem-solving scenarios, such as generating synthetic data for training machine learning models, designing optimized structures in engineering, or simulating complex systems for research purposes. By mastering generative AI, individuals gain a powerful toolset for addressing real-world challenges.
Career Opportunities: With the increasing integration of AI technologies across industries, proficiency in generative AI can open up numerous career opportunities. From roles in software development and data science to creative fields like design and entertainment, individuals with generative AI skills are in high demand and can command competitive salaries in the job market.
Can Gen AI systems make mistakes?
A big yes. While it strives for accuracy and coherence in it’s response; there might be occasions where AI systems can misinterpret the context and provide information that’s outdated or inaccurate.
Large Language Models (LLM)
Large language models are advanced AI systems trained on vast amounts of text data, capable of understanding and generating human-like language. They exhibit impressive language comprehension, enabling them to perform tasks such as text generation, summarization, translation, and question answering.
Generative AI systems are based on large language models which forces users to understand how they are trained. Training a LLM involves feeding it vast amounts of text data to learn patterns and language nuances. The process begins with pre-training on a diverse dataset to impart general knowledge and language understanding to the model. Fine-tuning is the next step which involves training the LLM on a more specific dataset for a particular task or domain. During training, the model adjusts its internal parameters through iterations to minimize prediction errors and improve performance. Finally, validation datasets are used to monitor the model’s performance and prevent overfitting during training.
Despite their capabilities, challenges such as ethical concerns, biases, and energy consumption highlight the need for responsible development and deployment of these models.
Key advantages of Gen AI systems
Creative Output: Gen AI can generate novel and creative content, including images, music, and text, which can be valuable for artistic endeavors, content creation, and design projects.
Automation of Tasks: Gen AI can automate repetitive tasks, such as data entry, image generation, and text summarization, freeing up human resources for more complex and creative endeavors.
Personalization: Gen AI can analyze large datasets and user preferences to generate personalized recommendations and experiences, enhancing customer engagement and satisfaction in various industries such as e-commerce, entertainment, and marketing.
Problem-solving: Gen AI can assist in problem-solving by generating solutions, optimizing processes, and identifying patterns in data, enabling organizations to make data-driven decisions and innovate more effectively.
Efficiency and Scalability: Gen AI can improve efficiency and scalability by automating workflows, optimizing resource allocation, and accelerating innovation cycles, leading to cost savings and competitive advantages for businesses and organizations.
Known dis-advantages of Gen AI systems.
While Generative AI (Gen AI) offers numerous benefits, it also presents some disadvantages:
Ethical Concerns: Gen AI raises ethical concerns regarding the authenticity and ownership of generated content. There are debates surrounding the potential misuse of AI-generated content for spreading misinformation, creating fake news, and manipulating public opinion.
Bias and Discrimination: Gen AI models trained on biased datasets may inadvertently perpetuate biases and discrimination in generated content. This can lead to unfair outcomes, reinforce stereotypes, and exacerbate societal inequalities.
Security Risks: Gen AI poses security risks, including the potential for malicious actors to exploit vulnerabilities in AI systems to generate fake identities, bypass authentication mechanisms, or launch cyberattacks such as phishing scams and social engineering attacks.
Loss of Jobs: The automation capabilities of Gen AI may lead to job displacement and unemployment in certain industries, as AI systems can perform tasks more efficiently and cost-effectively than humans. This can have socio-economic implications and require workforce retraining and reskilling initiatives.
Hallucination/ Dependence on Data: Gen AI models require large volumes of high-quality data for training, which can be expensive, time-consuming, and resource-intensive. Additionally, reliance on data may raise privacy concerns and require compliance with data protection regulations. Moreover, if the data is biased or incomplete, it can lead to inaccurate or unreliable AI-generated outputs.
As per Mckinsey report, economic potential of Generative AI is $4.1 Trillion each year, that is 4.4% of the total economic output. Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Generative AI will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI.
How to unlock the potential of Gen AI?
To unlock the potential of Generative AI as an individual, one should learn and experiment by diving into learning Generative AI techniques/ tools/ frameworks. Experiment with small projects to understand its capabilities and limitations.
Continuous Learning is needed to keep up with the latest research, advancements, and best practices in Generative AI. Individual may want to share findings, insights, and projects output with the community in order to foster collaboration and exchange ideas, expertise for growth and learning.
The key is to also stay informed about ethical considerations surrounding generative AI, such as bias mitigation and privacy concerns; and ensure responsible usage and deployment of AI-generated content.
Be a prompt engineer
Prompt engineering is a strategic and creative endeavor that shapes the interactions between humans and artificial intelligence systems. This is the process of crafting effective and specific prompts to guide artificial intelligence systems. It involves designing questions or statements that elicit desired responses from AI models. It is crucial in influencing the output and behavior of AI, ensuring accuracy and relevance in the generated content. By carefully constructing prompts, engineers can control the direction and focus of AI models, leading to more tailored and useful responses.
The effectiveness of prompt engineering can greatly impact the overall user experience and usefulness of AI-powered tools and services.
Conclusion
In summary, prompt engineering serves as a strategic tool for harnessing the power of Generative AI, by guiding the model’s output towards desired outcomes. Crafting well-designed prompts can steer the generative AI model to produce outputs that align with specific goals or criteria, ensuring precision and relevance in the generated content. Prompt engineering can help mitigate biases in generative AI outputs by providing prompts that encourage fair and unbiased content generation, promoting diversity and inclusivity. And finally, tailoring prompts to specific domains or tasks enables generative AI models to produce content that is relevant and useful within those contexts, enhancing their practical applicability.
Every year 19-23 million tonnes of plastic waste leaks into aquatic ecosystems; polluting lakes, rivers and seas (UNEP). This pollution chokes marine wildlife, damages soil and poisons groundwater, and can cause serious health impacts. This is not the only contributor for environmental degradation, but there are many: burning of fossil fuel for Energy generation and transportation, hazardous chemicals used for agriculture and fashion industry and many more.
Increased consumptions is a distinguishing feature of 21st century where more products and services are made available than even before. Total global consumption of construction materials, ore and minerals, fossil fuels, and biomass is ten times of the volume that was in 1900 (Mills, 2012). Large scale farming practices supported the population growth, but widespread usage of fertilisers not only degraded the soil quality over the time but also contributed to environmental pollution in a much larger scale.
Over past 70 years, the world has experienced unprecedented growth, and as a result, extraction of the natural resources was a must to fuel the growth. Currently, the population uses the equivalent of 1.7 of earth planet to provide resources needed to produce the goods and absorb waste. This means earth takes 1 year and 7 months to regenerate what has been used in 1 year (Global Footprint Network, 2020). With no much surprises, this plastic has reached the world’s deepest ocean trench – Mariana Trench which is roughly 11 kilometre below the sea surface.
We are able to raise awareness about environmental impacts and have made some good progress on sustainability reporting and monitoring in production, supply chains; but we have paid much less attention to the design of sustainable products, services and systems.
This is a global problem, YES. But this needs to be actioned at individual level to move the needle.
As an individual, planting trees to create green spaces is not practically possible, but taking actions which is within individual’s control will provide numerous environmental benefits. Adopting the “3 Rs” principle (Reduce, Reuse, Recycle) can significantly decrease waste generation.
Actions for individual
Say no to Single-Use Plastics: Avoid single-use plastics such as plastic bags, straws, and water bottles. Use reusable alternatives like cloth bags, stainless steel straws, and refillable water bottles to reduce plastic waste.
Choose Sustainable Transportation: Walking, biking, carpooling, or using public transportation will reduce greenhouse gas emissions.
Responsible Consumption: This includes conserve energy, save water, change food habit by reducing meat consumption (particularly from high-emission sources like beef and lamb). What about choosing locally sourced, organic, and seasonal foods to minimize the environmental impact of food production and transportation? Choosing products with minimal packaging, made from recycled materials, and produced using sustainable practices will add to the bigger goal to protect environment.
Advocate for Change: Raise awareness, support policies and initiatives that promote environmental protection and sustainability. Prioritise and speak environmental issues and advocate for sustainable practices in your community and workplace.
By incorporating these practices into their daily lives, individuals can contribute to a healthier planet and mitigate the impacts of climate change. Every small action adds up to make a meaningful difference in preserving the environment for future generations.
The Euclidean zoning used in urban planning divides the area into zones based on permitted uses, ignoring housing supply and liveable aspects. The zoning systems were designed with basic premises that people will buy cars and will prefer to travel to workplaces. This resulted into construction of wider roads and eventually roads got flooded with car, and prime locations in city had to accommodate parking spaces for car. Just imagine, millions of people spend their time on commuting to work place, not only this is a waste of productivity but also waste of resources in terms energy and negative externality like road congestion and resulting pollutions.
Bangalore city is one of the worst affected city in the world and resulted due to land zoning system followed for urban planning as stated above. People in Bangalore spend 243 hours on average every year for commuting to work place. (Business Today, 2020). The average commute time in Beijing is 52 minutes one way (South China Morning Post, 2015). And average american drive 25 miles to their workplace spending one hour on the road each day (Fatherly.com, 2020). Story is same for most of the modern cities. Time as a resource is well understood when you experience it in first hand. In fact, I started getting the benefit of having extra 2 hours in a day when I moved from Bangalore city to Singapore where I had 2 hours of daily commute hours to my workplace. This additional 2 hours allowed me to spend some quality time with family members and to pick up new hobbies.
How to Address
As per United Nations’ estimate, 55% of world’s population lives in cities (The World Bank, 2020) and drives 80% of the economic activities in terms of GDP. This population is growing at the rate of 2 to 3% year on year. Cities will play larger and larger roles in tackling climate change, they will contribute sustainable growth if managed well in terms of reducing carbon foot print, increasing productivity and allowing innovation to come through. Population of individual city can range from few hundred thousands to couple of millions (larger size city in the world has population as high as 30 million). We do not have luxury to displace millions of people to completely re-design the cities addressing infrastructure and housing issues. Instead, we need to apply right interventions through policy changes to address challenges discussed above related to overcrowding of roads, associated negative externality and improving productivity. Key considerations in my opinion are: (1) Affordable housing closer to workplace, (2) minimize/remove cars running on fossil fuel and (2) discourage vehicle ownership and provide access to various mobility options. Using alternate energy for cars will address the pollution/CO2 emission from the cars, but it will not address the road congestions and time spent on wheels while driving to workplace. The business model of “giving access to resources in contrary to owning them” has been successful in case of Uber where car-owners are enabled to ride-share, and Airbnb which enables home-owners to share their home space with travellers. Keeping these above considerations in mind, the following policy measures (not limited to) can be planned for cities:
Affordable Housing. Creating affordable housing within city limit, closer to workplace will address most of the challenges discussed above. Developers should be encouraged to build micro-units suitable for young professions with affordable rates and at closer proximity to workplace. This will open up alternate mobility options such as cycling, walking to consider while commuting to work, which will reduce road congestions and air pollution challenges in cities.
Cleaner-energy models. Gradually converting all cars and public transports to cleaner-energy models. This also can be done by provide more and more incentives for EVs. Country like Singapore has policy in place to incentivize EV users (straitstimes.com).
Ride-sharing. Re-adjusting existing infrastructure and incentivising high-occupancy vehicle (HOV) in terms of dedicating more lanes for ride-sharing, reducing road tolls will not only promote ride-sharing in cities but also will reduce the number of vehicles on the roads. This will in turn free-up lanes in the city those can be repurposed for other uses.
Autonomous vehicle. As technology make advancements, autonomous vehicle will potentially reduce number of accidents happening daily due to human error. This will also free-up commuters’ time spent on the wheel and keeping a watch on road conditions. City needs to provide various mobility options for it’s citizens to choose from. Promoting ride-sharing culture along with autonomous vehicles will have a larger benefits for a city. This combination will address both: road congestion problem resulting into CO2 emission, and land scarcity caused by parking lots covering the prime locations in a city. Singapore among other countries has come-up with policies to promote use of autonomous vehicle to address road congestion and land availability in city area (www.smartnation.gov.sg).
Conclusion
Technology has been a big enabler in making our cities smarter and efficient. Internet of Things (IoT) is able to address many of the challenges a city is facing in areas of assets management, energy distribution, water supply, sewage and others, by providing real-time insights; and Artificial Intelligence (AI) models being used to predict, take course corrections before an incident happens. Shared economy we discussed above can leverage Blockchain technology for peer-peer transactions. A smart contract between the asset owner and the consumer will initiate payment once transaction is executed successfully without any intermediary.
Technology alone can’t solve the problem and bring larger-scale social change, it requires collective participations from individuals as well. Some of the behavioural changes expected are as follows but not limited to:
Ride sharing culture otherwise known as “Shared Economy”, getting access to resources vs owning it.
Use what you really need. Manage with micro-units which can be easily converted into multipurpose usage rather than holding into larger housing unit and not optimally using the space.
Be accountable and be responsible while using resources and even shared resources. Taking short shower can save few liters of water, turning off the air conditioners and keeping windows open during morning and evening hours will reduce energy usage and CO2 emission.
Smart city is not a new concept, it exists for more than a decade now. Rather, I would say the definition of Smart city has changed over the time. During early 2000, definition of Smart City was more towards, connecting all disparate systems like land management system, traffic, telecommunication, electricity network, water supply, sewerage system; to operate the city in a seamless fashion. Focus was more on responding to crisis; either caused by nature or man-made, orchestrating various sub systems of a city to work in tandem and act in a wholistic manner. I call this as smart city 1.0. In the second wave, it was more towards deploying various types of technology methods, sensors devices to collect real-time data and use the Insights gained from those data to better manage the assets and resources. In this Smart City 2.0, focus was more on making city services more efficient by making sure bottlenecks in road infrastructure, energy distributions, sewerage network are identified and addressed before even they arise. Now we are in 3rd wave of Smart city which I termed as Smart City 3.0, where the focus is to address the human aspects, the community aspects, to drive economic growth and at the same time to improve the quality of life and improve collaboration among citizens. Extreme urbanization has got many challenges for the cities, either people have migrated from rural areas to the city centers or people live in city’s outskirts and drive to their work place as housing near city centers are not affordable. This housing crisis is due to Euclidean zoning where city divides land into residential, commercial, and industrial areas that resulted into road congestions as people travel long distance to work on daily basis.
Following are key characteristics of the smart city 3.0 where technology as enabler, is going to play major role in transforming it:
Making city more liveable. More than 50% of city’s landscape is occupied by roads and parking lots (Gardner, 2011), cars on the road are oversized, no place for pedestrians, and not enough playgrounds for kids. Challenge is to plan/design our cities for people and not for machines.
Affordable housing. Housing in cities became expensive for young professionals and lower income group citizens. Ideas and plan for affordable housing for all sections of people to be promoted.
Reduced traffic congestion. City to offer workplaces closer to affordable housing and provide access to all the services like schools, restaurants, shopping and other within walking distance where they live. This will not only cut down traffic congestions but will promote collaborations among citizens.
Ride sharing. Electric vehicles will solve the air pollution problem, but wouldn’t address the road congestion challenges that cities are facing today. Culture of ride sharing needs to be nurtured in the city. Research has shown that the risk of collision between pedestrians and motorised vehicles reduces when more number of people starts riding cycles (Elvik, 2009).
Innovation Districts: Establishing innovation districts in city center with closer proximity to university will attract young talents into the city, will promote new and diverse ideas.
Covid-19 pandemic has not only exposed how unprepared the entire world is, but also surfaced social and economic inequalities in the community. The administration and vision of all world leaders are being tested during this time. Role of independent international bodies like WHO has been doubted and there could be drastic changes going forward. Carrying out business remotely with minimum mobility and decreased levels of industrial activities have brought down pollution levels in urbanised areas. This has resulted into daily global CO2 emissions to decrease by –17%; by far the ONLY positive externality of the Covid-19 pandemic (Nature.com, 2020). New economic policies need to be established quickly to cushion the impact of declining world’s GDP as production units are severely affected by shutdown caused due to pandemic. Immediate priority for each nation is to contain the outbreak and at the same time to maintain a sustainable level of economic activities. Each nation is also constrained with technical capabilities and available capacity in terms of healthcare professionals, medical infrastructure; this is where private public partnership can come handy to address the crisis jointly.
As world is desperately looking for a comeback on it’s economic activities, we all need to play our part by following safe distance measures and quarantine rules imposed by the respective governments. IBM has developed offerings for Worker Insight Solutions which receives inputs from optical and thermal imaging cameras, Bluetooth beacons and mobile phones to provide analytical insights to workers and supervisors for a wide range of critical use cases targeting for COVID-19 situation.
