Lapsset Corridor could become a “Data Corridor”

AGI to be used in Lapsset Corridor

Data is the lifeblood of decision-making and the raw material for accountability. As AGI & strong AI is developed it will be used to assist us within the Lapsset Corridor – Energy, Water & Waste projects for analysis, monitoring, data collection, emission prediction, Energy Efficiency, Circular Economy and Climate Change Mitigation.

3 critical success factors for AI initiatives

• Aligned business strategy: Focus on building a value story, which illustrates progress toward business outcomes, tied to an organization’s business strategy. The more you are willing to rethink and recalibrate your business, the greater the potential impact of AI.
• Collaborative orchestration: There will be many concurrent AI and GenAI initiatives that require functional, technical, security, D&A and many other organizational resources. For the best chance of success, orchestrate them together. 
• Multidisciplinary governance: One-size-fits-all models are too restrictive, while siloed governance limits coordination and leads to inconsistent behavioral standards throughout the organization.

Take a synergistic, multidisciplinary and iterative approach

• Areas of strategy, orchestration and governance include:
  • Strategic business goals and alignment
  • Operation execution 
  • Measurement 
  • AI-ready data strategy and ongoing curation 
  • Security 
  • Governance 
  • Risk and regulatory 
  • Talent shifts and dexterity (inclusive of AI literacy and trust program) 
  • Trust level and behavioral shifts in decision making 
  • Ethics and compliance
• None of this is a one-time task. Multidisciplinary teams must iteratively revisit each item and dynamically adjust using a risk-adjusted, repeatable approach.

The volume of data in the world is increasing exponentially. In 2020, 64.2 zettabytes of data were created, that is a 314 percent increase from 2015. An increased demand for information due to the COVID-19 pandemics also contribute to higher-than-expected growth.

An invaluable mobile app helping Kenyan pastoralists beat the drought in August 2018. The UN’s Open SDG Data Hub will enable data providers, managers and users to discover, understand, and communicate patterns and interrelationships in the wealth of Sustainable Development Goal data and statistics that are now available. Photo: ITU/Trans.Lieu

Big Data for Development and Humanitarian Action

In 2015, the world embarked on a new development agenda underpinned by the Sustainable Development Goals (SDGs). Achieving these goals requires integrated action on social, environmental and economic challenges, with a focus on inclusive, participatory development that leaves no one behind.  

Critical data for global, regional and national development policymaking is still lacking. Many governments still do not have access to adequate data on their entire populations. This is particularly true for the poorest and most marginalized, the very people that leaders will need to focus on if they are to achieve zero extreme poverty and zero emissions by 2030, and to ‘leave no one behind’ in the process.

SDG API

The Sustainable Development Goals indicators database provides transparency on the data used for global reporting. The database contains data on the global Sustainable Development Goal indicators used in the Sustainable Development Goals Report 2018, and includes country-level data as well as regional and global aggregates.

The global Sustainable Development Goal indicators API gives programmatic access to the global indicators database using the OpenAPI specification.

The database, maintained by the Statistics Division, released on 20 June 2018 contains over 1.7 million observations. However, this is not the number of unique observations, as several indicators and their data are repeated. For the complete list of the indicators that are repeated in the indicator framework please see https://unstats.un.org/sdgs/indicators/indicators-list/ .

Data Centres in Kenya

Data centres now and in the future will use a lot of Energy. It is estimated that there are around 26 data centers in Kenya. These data centers are used to store and process data for various businesses and organizations in the country. The number of data centers in Kenya is expected to grow as the demand for data storage and processing services continues to increase within the Lapsset Corridor.

Data center components require significant infrastructure to support the center’s hardware and software. These include power subsystems, uninterruptible power supplies (UPS), ventilation, cooling systems, fire suppression, backup generators, and connections to external networks.

Growth rate

The average growth rate of data centers worldwide is estimated to be around 15% annually. This growth is driven by increased demand for data storage and processing services due to the proliferation of digital data, cloud computing, and big data analytics. The demand for data centers is expected to continue to grow as more businesses and organizations rely on data-driven decision making and digital technologies. However, the growth rate can vary by region and market conditions. In countries like Kenya, the growth rate of data centers may be higher due to the increasing adoption of digital technologies and the growing economy.

Environmental impacts – AI and the green energy transition will bring new challenges and opportunities

Data centers have several environmental impacts, including:

1. Energy consumption: Data centers consume a significant amount of energy to power servers, cooling systems, and other equipment. This high energy consumption contributes to greenhouse gas emissions and increases the demand for electricity, which can lead to environmental degradation. On average, a typical AI chat session may consume approximately 0.0003 kWh.
2. Carbon footprint: The energy consumed by data centers, often generated from fossil fuels, contributes to their carbon footprint. This adds to the overall carbon emissions and accelerates climate change.
3. Water consumption: Data centers require large amounts of water for cooling purposes, which can put a strain on local water resources, especially in areas experiencing water scarcity. One AI chat session results in 500ml of water use.
4. E-waste: The rapid turnover of IT equipment in data centers contributes to electronic waste (e-waste) generation. Improper disposal of this e-waste can lead to environmental pollution and health hazards.
5. Land use and habitat destruction: Data centers often require large amounts of land for construction and infrastructure. This can lead to habitat destruction, deforestation, and loss of biodiversity in the surrounding areas.

To mitigate these environmental impacts, data centers can implement sustainable practices such as using renewable energy sources, improving energy efficiency, recycling e-waste, and reducing water consumption. Additionally, industry regulations and certifications, such as LEED (Leadership in Energy and Environmental Design), can help promote environmentally responsible practices in data center operations.

The amount of energy and water used in one AI chat session can vary depending on several factors, such as the complexity of the conversation, the AI model used, the server infrastructure, and the data processing requirements.

In general, a single AI chat session typically consumes a relatively small amount of energy and water compared to other activities that require more intensive computing resources, such as running a data center or training a deep learning model. However, the cumulative impact of multiple AI chat sessions over time can still contribute to overall energy and water consumption, especially as AI use becomes more widespread.

Specific data on the energy and water consumption of one AI chat session is not readily available, as it can be influenced by various factors and is often considered proprietary information by companies that provide AI chat services.

To reduce the environmental footprint of AI chat services, companies can implement energy-efficient infrastructure, optimize algorithms for resource efficiency, and utilize renewable energy sources. Additionally, users and organizations can be mindful of their AI usage and make conscious choices to minimize energy and water consumption in their interactions with AI chat systems.

The exponential progress of artificial intelligence (AI) and machine learning is fueling a wave of transformative shifts in data center design, site selection, and investment strategies.

To keep up with the growing demand for computational power, hyperscale data centers are projected to increase their rack density at a compound annual growth rate (CAGR) of 7.8%.

By 2027, average rack density is set to reach 50kW per rack, surpassing the current average of 36kW.

^{Source: JLL Research, 2024}

Meanwhile, the data center industry faces mounting pressure to enhance energy efficiency and to fulfill renewable energy goals while meeting future demand.

Our latest research delves into the crucial considerations for data center developers and operators as they navigate the profound impact of power on the industry.

Big Data for Development and Humanitarian Action

In 2015, the world embarked on a new development agenda underpinned by the Sustainable Development Goals (SDGs). Achieving these goals requires integrated action on social, environmental and economic challenges, with a focus on inclusive, participatory development that leaves no one behind.  

Critical data for global, regional and national development policymaking is still lacking. Many governments still do not have access to adequate data on their entire populations. This is particularly true for the poorest and most marginalized, the very people that leaders will need to focus on if they are to achieve zero extreme poverty and zero emissions by 2030, and to ‘leave no one behind’ in the process.

Big data can shed light on disparities in society that were previously hidden. For example, women and girls, who often work in the informal sector or at home, suffer social constraints on their mobility, and are marginalized in both private and public decision-making.

Much of the big data with the most potential to be used for public good is collected by the private sector. As such, public-private partnerships are likely to become more widespread. The challenge will be ensuring they are sustainable over time, and that clear frameworks are in place to clarify roles and expectations on all sides.

Environmental Management System (EMS)

Please watch the video

Artificial General Intelligence (AGI) refers to a type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge in a manner similar to human intelligence. Unlike narrow AI, which is designed for specific tasks, AGI is intended to demonstrate cognitive abilities across a wide range of tasks, similar to the flexibility and reasoning capabilities of human beings.

AGI is often considered the ultimate goal of AI research, as it aims to replicate human-like intelligence and general problem-solving abilities in machines. Achieving AGI could have profound implications for various fields, including Environmental Management, Climate Change Mitigation, automation, robotics, healthcare, and more.

In the context of the Lapsset Corridor project in Kenya, which focuses on infrastructure and economic development to connect Kenya with other countries in East Africa, AGI could be utilized in various ways to enhance renewable energy development and climate change mitigation efforts:

AGI uses

1. Energy Efficiency: AGI can be used to optimize energy usage in buildings and infrastructure within the Lapsset Corridor, leading to improved energy efficiency and reduced carbon emissions. AGI systems can analyze data from sensors and devices to automatically adjust energy consumption based on real-time conditions.
2. Climate Modelling: AGI can help in predicting climate patterns and extreme weather events in the region, enabling better planning and management of renewable energy projects and climate change mitigation strategies. By analysing large amounts of historical and real-time data, AGI systems can provide valuable insights for decision-making.
3. Smart Grid Management: AGI can be applied to optimize the operation of renewable energy sources, such as solar and wind farms, and integrate them into the existing power grid more efficiently. By using AGI algorithms to predict energy demand and supply variations, energy distribution can be optimized to reduce wastage and increase reliability.
4. Environmental Monitoring: AGI can analyse satellite imagery and sensor data to monitor environmental changes, such as deforestation, land degradation, and wildlife habitat loss along the Lapsset Corridor. This information can guide conservation efforts and help prevent negative impacts on ecosystems caused by infrastructure development.

Overall, integrating Artificial General Intelligence into the Lapsset Corridor project can enhance sustainability, efficiency, and resiliency in the region’s renewable energy and climate change mitigation initiatives. By leveraging AGI technologies, Kenya can advance its green energy transition and contribute to global efforts to combat climate change.

Benefits of having an AGI system

Having an environmental management system (EMS) in place is important for organizations like the Lapsset Corridor project in Kenya for several reasons, including:

1. Compliance: An EMS helps ensure that the project complies with environmental regulations and standards set by local authorities and international bodies. This can help avoid penalties and legal issues resulting from non-compliance.
2. Risk management: Implementing an EMS allows the project to identify and mitigate potential environmental risks and impacts early on. This proactive approach can help prevent environmental incidents that may harm ecosystems and communities in the project area.
3. Reputation and stakeholder trust: Demonstrating a commitment to environmental sustainability through an EMS can enhance the project’s reputation and build trust with stakeholders, including local communities, government agencies, and investors. A positive environmental record can also attract funding and support for the project.
4. Cost savings: An EMS can help the project identify opportunities for cost savings through improved resource efficiency, waste reduction, and energy conservation. By minimizing environmental impacts, the project can also reduce operational costs associated with environmental cleanup and remediation.
5. Sustainable development: Incorporating environmental considerations into project planning and management through an EMS promotes sustainable development practices. This can help balance economic development with environmental protection and social responsibility, ensuring long-term benefits for the project and surrounding communities.

In the case of the Lapsset Corridor project in Kenya, implementing an EMS can help minimize the environmental impacts of infrastructure development, such as land use changes, habitat destruction, and water pollution. By integrating environmental management practices into the project’s planning and operations, Lapsset Corridor can enhance sustainability, protect ecosystems, and promote responsible resource use in the region. This can lead to positive outcomes for the environment, local communities, and the project’s long-term success.

AGI increases Energy & Water use

Artificial General Intelligence (AGI) refers to a hypothetical machine intelligence that has the ability to understand, learn, and apply knowledge in a manner similar to human intelligence. While AGI itself does not directly increase energy and water use in a country, the technologies and infrastructure required to develop and support AGI systems, such as data centers, can contribute to increased resource consumption.

When new data centers are built in Kenya to support AGI development or other IT activities, they can pose several challenges related to energy and water use, as well as additional problems, including:

1. Energy consumption: Data centers require substantial amounts of electricity to power servers, cooling systems, and other equipment. The increased energy demand from new data centers can strain the local power grid and contribute to higher greenhouse gas emissions if the electricity is generated from fossil fuels.
2. Water consumption: Data centers use water for cooling purposes, which can put pressure on local water resources, especially in regions facing water scarcity. In Kenya, where water availability is already a concern in some areas, the additional water demand from new data centers can exacerbate this challenge.
3. Land use and habitat degradation: Constructing new data centers requires land for buildings, infrastructure, and supporting facilities. This land use can lead to habitat destruction, deforestation, and loss of biodiversity, particularly if the data centers are built in environmentally sensitive areas.
4. E-waste generation: The rapid turnover of IT equipment in data centers can result in electronic waste (e-waste) generation. Improper disposal of this e-waste can lead to environmental pollution and health hazards if not managed properly.
5. Social and economic impacts: The construction and operation of new data centers may have social and economic implications, including land use conflicts, changes in local economies, and potential displacement of communities. It is important to consider the social and cultural context when siting and developing new data centers in Kenya.

To address these challenges and minimize the negative environmental and social impacts of new data centers, it is essential to adopt sustainable practices, such as using renewable energy sources, implementing energy-efficient technologies, optimizing water use, and responsibly managing e-waste. Engaging with local communities, conducting environmental impact assessments, and complying with regulations and standards can also help mitigate the additional problems associated with building new data centers in Kenya.

The energy usage of a data center can vary widely depending on factors such as size, location, efficiency measures, and the type of equipment being used. However, on average, a medium-sized data center can consume anywhere from a few hundred kilowatt-hours (kWh) to several megawatt-hours (MWh) of electricity per year.

For estimation purposes, let’s consider a medium-sized data center that consumes around 1,000 MWh of electricity per year. Some larger data centers can consume tens of thousands of MWh of electricity annually, while smaller data centers may require less than 100 MWh per year.

It’s important to note that efforts to increase energy efficiency, such as using low-power servers, implementing cooling technologies, and optimizing workload distribution, can help reduce the energy consumption of data centers. Utilizing renewable energy sources, implementing energy-saving policies, and improving hardware efficiency are also critical steps in minimizing the environmental impact of data center operations.

A Solar Farm could be used for these Data Centres

To calculate the energy generated from a 300 MW solar farm example in Isiolo, Kenya, we can use the formula:

Energy (MWh) = Power (MW) x Time (hours) x Capacity Factor

Assuming a capacity factor of around 20% for a typical solar farm, we can calculate the energy generated in MWh over a year:

Energy (MWh) = 300 MW x 9 hours x 365 days x 0.20 Energy (MWh) = 197,100 MWh

Therefore, a 300 MW solar farm in Isiolo, Kenya would generate approximately 197,100 MWh of electricity over the course of a year. This calculation is based on the capacity factor assumption and actual generation may vary depending on factors such as weather conditions, maintenance, and efficiency of the solar panels. Some of this energy can be used by a Data Centre.