{"id":15634,"date":"2024-05-28T07:54:23","date_gmt":"2024-05-28T07:54:23","guid":{"rendered":"https:\/\/dataforpublicgood.org.in\/?p=15634"},"modified":"2024-06-04T05:34:19","modified_gmt":"2024-06-04T05:34:19","slug":"sustainability-in-the-information-society","status":"publish","type":"post","link":"https:\/\/dataforpublicgood.org.in\/cdpg\/blog\/sustainability-in-the-information-society\/","title":{"rendered":"Sustainability in the Information Society"},"content":{"rendered":"\t\t
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In 1987, the World Commission on Environment and Development of the United Nations released Our Common Future, also known as the Brundtland Report. The report firmly established the modern paradigm of sustainable development, as \u201cdevelopment that meets the needs of the present without compromising the ability of future generations to meet their own needs\u201d.<\/p>

This definition established the inextricable linkage between \u201csustainability\u201d and \u201cdevelopment\u201d. Development that doesn\u2019t consider and factor in long-term socio-environmental objectives, implications and consequences is short sighted, inappropriate and un-sustainable. Sustainability visions, blueprints and plans that do not fully cater for the needs of the present, including economic development and the alleviation of poverty, are unrealistic and fundamentally untenable.<\/p>

In 2015, the UN further refined the concept, surveying more than a million people around the world, and distilling the results into 17 interlinked \u201cSustainable Development Goals\u201d or SDGs. These specify desirable societal objectives ranging from poverty alleviation to climate action to food security, along with a system of sub-goal targets and measurement to track progress on these.<\/p>

\"\"<\/p>

Figure 1 – The Sustainable Development Goals (SDGs)<\/figure>

While the \u201cwhat<\/strong>\u201d of sustainable development has been determined by a globally consultative process, the \u201chow<\/strong>\u201d of achieving these ambitious goals remains a challenging open problem for each country to determine and implement according to its particular context, development trajectory, resources and constraints. According to the UN, \u201cIndia is critical<\/a>\u201d in determining the success of the SDGs. A NITI Aayog report<\/a> also acknowledges that \u201cthe world cannot achieve the SDGs without India achieving them\u201d, and that Indian success stories can potentially guide other countries as well.<\/p>

Keeping this in mind, it\u2019s worthwhile asking: \u201chow\u201d may India achieve a sustainable development pathway, given its ongoing evolution into a digital economy and \u201cinformation society\u201d whose processes will be increasingly facilitated and governed through emerging data-based technologies, advanced public and private digital infrastructure and artificial intelligence?<\/p>

It would seem that while data is foundational for societal value creation in a smart governance \u2018stack\u2019, data collection cannot, in itself, solve complex and interlinked sustainable development challenges. For example, air quality sensors installed in a city can inform us regarding pollution in a spatio-temporally granular manner. However, moving to better outcomes in air quality might require cleaner emission standards, financing for electric vehicles, increased green cover, dust control measures, preventing open garbage burning, addressing agricultural stubble burning<\/a>, creating bicycle lanes and culture, developing efficient mass transit, prioritising innovation for air quality etc.<\/p>

While data and technology can contribute to each of these, an actual transformation in a city\u2019s air quality might require a \u201ctheory of change<\/a>\u201d with measurable outcomes to be developed and grounded through practice and experiment. How can data, digital infrastructure and emerging technologies underpin, enable and ground-truth theories of change for a sustainable development transformation?<\/strong><\/p>

Data Leverage for Systems Change<\/strong><\/h3>

Systemic, or \u201dsystems\u201d thinking<\/a>, can provide fertile ground, inspiration and tools for developing theories of change for sustainability transformations. Donella Meadows, pioneering systems scientist and one of the most influential environmental thinkers of the 20th century, observed that<\/a> sustainability requires \u201cstructural change\u201d and that \u201cinformation is the key\u201d to this transformation. Meadows also articulated an influential approach to finding \u201cleverage points<\/a>\u201d for systems to change. These are places within a complex system (such as a country\u2019s food system, or a city\u2019s transport system) where a small change could potentially significantly impact the system as a whole. The leverage points are displayed below, arranged in increasing order of effectiveness , as per her original presentation. They range from \u2018constants, parameters and numbers\u2019 to \u2018the power to transcend paradigms\u2019. How can data, digital infrastructure and emerging technologies be engaged into each form of leverage, towards creating systems change for a sustainability transformation?<\/strong><\/p>

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Figure 2-Leverage points for systems change<\/a> (Donella Meadows)<\/figure>

Let\u2019s number the leverage points from L12 (constants, parameters, numbers) to L1 (the power to transcend paradigms). L12 leverage is illustrated by the 2015 Paris Agreement on Climate Change<\/a> \u2013 to limit global temperature rise this century to within 2 degrees above pre-industrial levels. In Meadows\u2019 ordering, constants, numbers and parameters \u2013 like the 2 degrees \u201ctarget\u201d – provide the \u201cweakest\u201d leverage for systems change. (Is it any surprise, then, that climate action plans have made insufficient progress<\/a> to towards the goals of the Paris Agreement?)<\/p>

While L12 provides \u2018weak\u2019 leverage in general, data, and scientifically well-grounded modelling can certainly enable policy-makers to determine and set more accurate, appropriate and granular system parameters and setpoints. For example, in order to prevent over-fishing in a particular area, satellite imagery, big data analytics and marine biology modelling can be integrated to set precise limits for exactly how much fishing is to be permitted in a particular coastal area. A similar approach could be set to limit groundwater extraction for agriculture based on real-time data from soil moisture sensors and weather modelling.<\/p>

L11 refers to a system\u2019s \u2018buffers\u2019 \u2013 for example, a company\u2019s cash reserves, which guard against financial volatilities; or a forest along a river, that reduces flooding and erosion by absorbing excess rainwater. When a system\u2019s \u2018buffers\u2019 are \u2018undersized\u2019, it lacks the ability to adequately handle shocks and contingencies. Overlarge buffers might be prohibitively costly to build or operate. Data can help in appropriately sizing buffers in a system, providing leverage for increasing sustainability in a system. For example, a country\u2019s grain stockpiles can be inefficiently sized along supply chains. Undersized stockpiles can pose a challenge for food security in emergency conditions, like during a drought; oversized stockpiles can result in grain wastage due to spoiling. Integrating demand forecasting, supply chain data, weather and climate modelling, grain shelf life and risk modelling might enable better sizing of food security buffers for a system like the Public Distribution System (PDS). Another example of L11 data leverage is to utilise e-mobility trip data, variable electricity tariff rates, and traffic volume records to appropriately size and position battery-swap and charge station \u201cbuffers\u201d in a city\u2019s smart electricity grid.<\/p>

L10 leverage refers to changing the actual transit of materials in a system. The IUDX\u2019s Solid Waste Pickup Route Optimization<\/a> is a great example of data leverage in this regard; the system uses Artificial Intelligence and Machine Learning (AI\/ML) to optimise waste collection routes in several Indian Smart Cities for faster pickup, better fuel usage, reduced costs and increased citizen convenience.<\/p>

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Figure 3 – The circular economy – an industrial system that is restorative by design
(Ellen MacArthur Foundation)<\/a><\/figure>

Another compelling example of L10 leverage is the potential transition to a \u2018circular\u2019 economy<\/a> \u2013 moving away from the current unsustainable \u2018linear\u2019 take-make-dispose system of production and consumption. Inspired by natural cycles and \u2018industrial\u2019 ecology<\/a>, this concept sees materials and biochemicals flow in \u201ccircular\u201d and \u201ccascading\u201d value-chains<\/a>. The circular economy concept<\/a> goes far beyond just recycling waste, to include concepts like reverse product logistics, refurbishing, substituting materials, reducing or eliminating material use (dematerialisation) and comprehensive product life-cycle management. Data and emerging technologies will play a pivotal role in enabling these<\/a>, through tools such as life-cycle assessment (LCA) databases<\/a>, virtual material passports and digital twin<\/a> technologies that can promote enhanced sustainability and circularity across value chains.<\/p>

L9 \u2013 changing the length of \u2018delays\u2019 in the system, is an interesting source of leverage where data can play a key role. As an example of this, consider a water utility\u2019s distribution system. India currently faces a reported \u201cNon-Revenue Water\u201d<\/a> of 38% – meaning that of every 100 litres of water produced, only 62 litres are billed or metered at the point of consumption. Installing Internet-connected water flow meters across the utility\u2019s distribution networks, and using these with backend analytics, algorithms and AI\/ML can help to quickly identify and fix leaks in the system. This \u201creduction of delay\u201d between the occurrence of a leak and plugging can shift the water system towards greater sustainability. This is especially important in a country like India, where an estimated 600 million people faced water stress in 2018, according to a NITI Aayog report<\/a>. Another example of L9 in a smart city is IUDX\u2019s Intelligent Traffic Signal<\/a> System, which reduces the \u201cdelay\u201d between detecting traffic congestion and adjusting traffic light schedules, compared to conventional traffic signals which use pre-set clocks.<\/p>

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Figure 4-Negative feedback in homeostatic regulation (Khan Academy<\/a>)<\/figure>

L8 leverage refers to adjusting the \u201cstrength\u201d of stabilising negative feedback \u201cloops\u201d in systems. Negative feedback occurs widely in nature and generates \u2018self-regulating\u2019 behaviours, for example in predator-prey dynamics<\/a> and in the human body\u2019s homeostatic<\/a> capabilities. A societal system like the marketplace is also governed to large extent by negative feedback and displays self-regulating behaviour \u2013 through \u201cprice signals\u201d which work to moderate demand and supply of commodities, and keep them in a dynamic balance, or \u2018equilibrium<\/a>\u2019. In certain cases, though, \u201cmarket failure<\/a>\u201d can arise when the price signals do not adequately \u201ccapture the costs\u201d that society bears for market transactions. For example, industries might pollute the air or water through production activities, but not pay the true cost of these due to poor or infeasible regulation. Since the cost of pollution is not correctly \u201cinternalised\u201d in the price of goods and services, businesses and consumers in the economy do not have adequate incentive to reduce or limit their pollutive activities. This can result in a socially undesirable \u201coverproduction of pollution\u201d in the economy.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\tIf the costs associated with industrial air and water pollution could be quickly and accurately measured (or estimated) and \u201creinternalised\u201d so that the offending corporations have to bear it, the market failure could be corrected, and economic incentives more correctly aligned towards limiting pollution. Towards this, Internet-connected air and water quality sensors could be densely installed in industrial clusters, for example near smokestacks and water exhaust streams. These could be augmented with satellite data, and potentially processed through advanced analytics, air<\/a> and water<\/a> quality dispersion modelling, and AI algorithms to spot polluters<\/a>, estimate \u201cpollution taxes<\/a>\u201d, and strengthen negative feedback steering towards a cleaner economy. An analogous process could potentially leverage satellite data to track carbon emissions<\/a> and provide corrections through a carbon tax or other instruments.\n\nL7 \u2013 adjusting the gain around positive feedback loops \u2013 is an interesting source of leverage that pertains to \u201cgrowth processes\u201d in society. These could include economic and population growth, financial interest rates, the spread of communicable diseases etc. Meadows\u2019 own explorations of this culminated with the 1972 publication of The Limits to Growth<\/a>, which used systems dynamics modelling<\/a> to show that unfettered economic and population growth can have self-limiting and potentially catastrophic \u201covershoot-and-collapse\u201d consequences.\n\nA contemporary exploration of this leverage point could understand, for example, the relationship between climate change and its implications for a country\u2019s growth. Integrated Assessment Models<\/a> (IAMs) provide a means to study this, by coupling economic and climate systems in a single framework. This allows the \u201cimpacts\u201d of climate change on the economy and human livelihoods to be modelled; the economic \u201ccosts\u201d and \u201cbenefits\u201d of decarbonisation to be studied; and policies to be developed to enable industries and communities to \u201cadapt\u201d to unavoidable climate change. This is especially important for India, whose growth is \u201clinked intricately\u201d with climate risks; these have a \u201cdisproportionate impact on vulnerable communities\u201d and impede their sustainable development, according to a CEEW report<\/a>.\n

Deep Leverage, Information and Paradigms<\/strong><\/h3>\nSo far, we have looked at the leverage points L12 to L7 in Meadows\u2019 hierarchy of interventions for systems change. These correspond to adjusting parameters, buffers, material flows and feedback in complex systems. These aspects lend themselves especially well to data-based quantitative approaches, AI\/ML, modelling and control, as we saw in the last section.\n\n\"\"\n
Figure 5 – Deep & shallow leverage points, and their interaction\n(extracted from Fischer & Riechers<\/a>, 2019)<\/figure>\nThe leverage points L6 (structure of information flows) to L1 (the power to transcend paradigms) challenge us to seek a different, deeper kind of leverage, focusing on a system\u2019s \u2018intent\u2019 (L3 to L1) and \u2018design\u2019 (L6 to L4). Changes at these levels, which include humanistic, institutional, societal and cultural considerations, can lead to transformative and lasting changes in system behaviour and outcomes, especially since the \u2018shallower\u2019 leverage points (L12-L7) tend to realign with the deeper ones through dynamic interactions, as illustrated in Figure 5. In other words, if \u2018intention\u2019 changes, design follows, and from there further leverage engages, related to processes and material. The power to \u201ctranscend paradigms\u201d is seen as being the greatest source of change in systems, and the world.\n\nOne of the most profound \u201cparadigm shifts<\/strong>\u201d that has occurred in recent years has been the emergence of a new \u201ccontext-driven\u201d mode of research<\/a> that seeks to produce \u201cknowledge for application<\/strong>\u201d. This \u201dMode 2\u201d Science<\/a> goes far beyond typical academic models, and extends the empirical method to real-world societal challenges and problem-solving. Inter-disciplinary teamwork and multi-stakeholder engagement are key aspects of creating and deploying scientifically well-grounded solutions in real-world theatres.\n\nOur ongoing sustainability challenges are matched with unfolding opportunities to create systems change leveraging data and emerging technologies. How can the academy bring scientific excellence to bear on these? How can researchers engage in new and beneficial ways with innovators, technology startups, digital infrastructure operators, policy-makers, citizens and communities, industries and other relevant stakeholders? How can we collaborate to develop well-grounded theories-of-change, underpinned by contextually appropriate and transformative technological solutions? How can we together \u201cachieve sustainability\u201d in the \u201cinformation society\u201d that we have the opportunity to design and create?<\/strong>\n

Author:<\/b><\/p>\nHARI DILIP KUMAR<\/b>\n

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In 1987, the World Commission on Environment and Development of the United Nations released Our Common Future, also known as the Brundtland Report. The report firmly established the modern paradigm of sustainable development, as \u201cdevelopment that meets the needs of the present without compromising the ability of future generations to meet their own needs\u201d. This definition established the inextricable linkage …<\/p>\n","protected":false},"author":2,"featured_media":15645,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"class_list":["post-15634","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"\nSustainability in the Information Society<\/title>\n<meta name=\"description\" content=\"This definition established the inextricable linkage between \u201csustainability\u201d and \u201cdevelopment\u201d. Development that doesn\u2019t consider and factor in long-term socio-environmental objectives, implications and consequences is short sighted, inappropriate and un-sustainable. Sustainability visions, blueprints and plans that do not fully cater for the needs of the present, including economic development and the alleviation of poverty, are unrealistic and fundamentally untenable.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dataforpublicgood.org.in\/cdpg\/blog\/sustainability-in-the-information-society\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sustainability in the Information Society\" \/>\n<meta property=\"og:description\" content=\"This definition established the inextricable linkage between \u201csustainability\u201d and \u201cdevelopment\u201d. Development that doesn\u2019t consider and factor in long-term socio-environmental objectives, implications and consequences is short sighted, inappropriate and un-sustainable. 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