Ahlsell – Ahlsell’s sustainability report 2025 shows continued progress in the sustainable transition

EMR Analysis

More information on Ahlsell: See the full profile on EMR Executive Services

More information on Claes Seldeby (President and Chief Executive Officer, Ahlsell Group): See the full profile on EMR Executive Services

More information on Kristian Ackeby (Chief Financial Officer, Ahlsell Group): See the full profile on EMR Executive Services

 

More information on the Ahlsell Sustainability Strategy and Sustainability Report 2025: See full profile on EMR Executive Services

More information on Christina Lindbäck (Chief Sustainability Officer, Ahlsell): See full profile on EMR Executive Services

 

 

 

More information on The Science Based Targets initiative (SBTi): https://sciencebasedtargets.org/ + The Science Based Targets initiative (SBTi) is a global body enabling businesses to set ambitious emissions reductions targets in line with the latest climate science. It is focused on accelerating companies across the world to halve emissions before 2030 and achieve net-zero emissions before 2050.

There are two main types of science-based targets: near-term and net-zero. Near-term targets aim to address emissions reductions over the next 5-10 years, whereas net-zero targets include reductions of 90% or more no later than 2050.

The initiative is a collaboration between CDP, the United Nations Global Compact, World Resources Institute (WRI) and the World Wide Fund for Nature (WWF) and one of the We Mean Business Coalition commitments. The SBTi defines and promotes best practice in science-based target setting, offers resources and guidance to reduce barriers to adoption, and independently assesses and approves companies’ targets.

• Defines and promotes best practices in emissions reductions and net-zero targets in line with climate science.
• Provides target setting methods and guidance to companies to set science-based targets in line with the latest climate science.
• Includes a team of experts to provide companies with independent assessment and validation of targets.
• Serves as the lead partner of the Business Ambition for 1.5°C campaign, an urgent call to action from a global coalition of UN agencies, business and industry leaders that mobilizes companies to set net-zero science-based targets in line with a 1.5 degrees C future.

More information on David Kennedy (Chief Executive Officer, SBTi): https://sciencebasedtargets.org/about-us/the-team + https://www.linkedin.com/in/david-kennedy-1000a3262/ 

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Carbon Dioxide (CO2):
  • The primary greenhouse gas emitted through human activities. Carbon dioxide enters the atmosphere through burning fossil fuels (coal, natural gas, and oil), solid waste, trees and other biological materials, and also as a result of certain chemical reactions (e.g., manufacture of cement). Carbon dioxide is removed from the atmosphere (or “sequestered”) when it is absorbed by plants as part of the biological carbon cycle.
• Biogenic Carbon Dioxide (CO2):
  • Biogenic Carbon Dioxide (CO2) and Carbon Dioxide (CO2) are the same molecule. Scientists differentiate between biogenic carbon (that which is absorbed, stored and emitted by organic matter like soil, trees, plants and grasses) and non-biogenic carbon (that found in all other sources, most notably in fossil fuels like oil, coal and gas).
• CO2e (Carbon Dioxide Equivalent):
  • CO2e means “carbon dioxide equivalent”. In layman’s terms, CO2e is a measurement of the total greenhouse gases emitted, expressed in terms of the equivalent measurement of carbon dioxide. On the other hand, CO2 only measures carbon emissions and does not account for any other greenhouse gases.
  • A carbon dioxide equivalent or CO2 equivalent, abbreviated as CO2-eq is a metric measure used to compare the emissions from various greenhouse gases on the basis of their global-warming potential (GWP), by converting amounts of other gases to the equivalent amount of carbon dioxide with the same global warming potential.
    • Carbon dioxide equivalents are commonly expressed as million metric tonnes of carbon dioxide equivalents, abbreviated as MMTCDE.
    • The carbon dioxide equivalent for a gas is derived by multiplying the tonnes of the gas by the associated GWP: MMTCDE = (million metric tonnes of a gas) * (GWP of the gas).
    • For example, the GWP for methane is 25 and for nitrous oxide 298. This means that emissions of 1 million metric tonnes of methane and nitrous oxide respectively is equivalent to emissions of 25 and 298 million metric tonnes of carbon dioxide.
• Carbon Footprint:
  • There is no universally agreed definition of what a carbon footprint is.
  • A carbon footprint is generally understood to be the total amount of greenhouse gas (GHG) emissions that are directly or indirectly caused by an individual, organization, product, or service. These emissions are typically measured in tonnes of carbon dioxide equivalent (CO2e).
  • In 2009, the Greenhouse Gas Protocol (GHG Protocol) published a standard for calculating and reporting corporate carbon footprints. This standard is widely accepted by businesses and other organizations around the world. The GHG Protocol defines a carbon footprint as “the total set of greenhouse gas emissions caused by an organization, directly and indirectly, through its own operations and the value chain.”
• Decarbonization:
  • Reduction of carbon dioxide emissions through the use of low carbon power sources, and achieving a lower output of greenhouse gases into the atmosphere.
• Carbon Credits or Carbon Offsets:
  • Permits that allow the owner to emit a certain amount of carbon dioxide or other greenhouse gases. One credit permits the emission of one ton of carbon dioxide or the equivalent in other greenhouse gases.
  • The carbon credit is half of a so-called cap-and-trade program. Companies that pollute are awarded credits that allow them to continue to pollute up to a certain limit, which is reduced periodically. Meanwhile, the company may sell any unneeded credits to another company that needs them. Private companies are thus doubly incentivized to reduce greenhouse emissions. First, they must spend money on extra credits if their emissions exceed the cap. Second, they can make money by reducing their emissions and selling their excess allowances.
• Carbon Capture and Storage (CCS) – Carbon Capture, Utilisation and Storage (CCUS):
  • CCS involves the capture of carbon dioxide (CO2) emissions from industrial processes. This carbon is then transported from where it was produced, via ship or in a pipeline, and stored deep underground in geological formations.
  • CCS projects typically target 90 percent efficiency, meaning that 90 percent of the carbon dioxide from the power plant will be captured and stored.
  • CCUS adds the utilization aspect, where the captured CO2 is used as a new product or raw material.
• Carbon Dioxide Removal (CDR) or Durable Carbon Removal: 
  • Carbon Dioxide Removal encompasses approaches and methods for removing CO2 from the atmosphere and then storing it permanently in underground geological formations, in biomass, oceanic reservoirs or long-lived products in order to achieve negative emissions.
  • Direct Air Capture (DAC): 
    • Technologies that extract CO2 directly from the atmosphere at any location, unlike carbon capture which is generally carried out at the point of emissions, such as a steel plant.
    • Constraints like costs and energy requirements as well as the potential for pollution make DAC a less desirable option for CO2 reduction. Its larger land footprint when compared to other mitigation strategies like carbon capture and storage systems (CCS) also put it at a disadvantage.
  • Direct Air Capture and Storage (DACCS):
    • Climate technology that removes carbon dioxide (CO2) directly from the ambient atmosphere using large fans and chemical processes to bind with the CO2.
  • Bioenergy with Carbon Capture and Storage (BECCS):
    • Negative emissions technology that captures carbon dioxide (CO2) from biomass used for energy production and stores it permanently. Plants absorb CO2 from the atmosphere as they grow (photosynthesis), and BECCS interrupts the cycle by capturing this biogenic CO2 during the energy conversion process—burning, fermentation, etc.—instead of letting it re-enter the atmosphere.
  • Enhanced Rock Weathering (ERW):
    • Carbon dioxide removal (CDR) technique that accelerates the natural process of rock weathering by grinding silicate rocks into dust and spreading it on land, typically agricultural fields. This process uses rainwater to convert atmospheric carbon dioxide into mineral carbonates, which are then stored long-term in soils, groundwater, and oceans.
• Limits of Carbon Dioxide Storage: 
  • Carbon storage is not endless; the Earth’s capacity for permanently storing vast amounts of captured carbon, particularly in geological formations, is limited, potentially reaching a critical limit of 1,460 gigatonnes at around 2200, though storage durations vary significantly depending on the method, from decades for some biological methods to potentially millions of years for others like mineralization. While some methods offer very long-term storage, the sheer volume needed to meet climate targets requires scaling up storage significantly beyond current capacity, raising concerns about the available volume over time.

 

• Global Warming: 
  • Global warming is the long-term heating of Earth’s climate system observed since the pre-industrial period (between 1850 and 1900) due to human activities, primarily fossil fuel burning, which increases heat-trapping greenhouse gas levels in Earth’s atmosphere.
• Global Warming Potential (GWP): 
  • The heat absorbed by any greenhouse gas in the atmosphere, as a multiple of the heat that would be absorbed by the same mass of carbon dioxide (CO2). GWP is 1 for CO2. For other gases it depends on the gas and the time frame.
  • Carbon dioxide equivalent (CO2e or CO2eq or CO2-e) is calculated from GWP. For any gas, it is the mass of CO2 which would warm the earth as much as the mass of that gas. Thus it provides a common scale for measuring the climate effects of different gases. It is calculated as GWP times mass of the other gas. For example, if a gas has GWP of 100, two tonnes of the gas have CO2e of 200 tonnes.
  • GWP was developed to allow comparisons of the global warming impacts of different gases.
• Greenhouse Gas (GHG):
  • A greenhouse gas is any gaseous compound in the atmosphere that is capable of absorbing infrared radiation, thereby trapping and holding heat in the atmosphere. By increasing the heat in the atmosphere, greenhouse gases are responsible for the greenhouse effect, which ultimately leads to global warming.
  • The main gases responsible for the greenhouse effect include carbon dioxide, methane, nitrous oxide, and water vapor (which all occur naturally), and fluorinated gases (which are synthetic).

• GHG Protocol Corporate Standard Scope 1, 2 and 3: https://ghgprotocol.org/ + The GHG Protocol Corporate Accounting and Reporting Standard provides requirements and guidance for companies and other organizations preparing a corporate-level GHG emissions inventory. Scope 1 and 2 are typically mandatory for companies that are required to report their emissions by national or regional regulations. The GHG Protocol itself is a voluntary standard.
  • Scope 1: Direct emissions:
    • Direct emissions from company-owned and controlled resources. In other words, emissions are released into the atmosphere as a direct result of a set of activities, at a firm level. It is divided into four categories:
      • Stationary combustion (e.g from fuels, heating sources). All fuels that produce GHG emissions must be included in scope 1.
      • Mobile combustion is all vehicles owned or controlled by a firm, burning fuel (e.g. cars, vans, trucks). The increasing use of “electric” vehicles (EVs), means that some of the organisation’s fleets could fall into Scope 2 emissions.
      • Fugitive emissions are leaks from greenhouse gases (e.g. refrigeration, air conditioning units). It is important to note that refrigerant gases are a thousand times more dangerous than CO2 emissions. Companies are encouraged to report these emissions.
      • Process emissions are released during industrial processes, and on-site manufacturing (e.g. production of CO2 during cement manufacturing, factory fumes, chemicals).
  • Scope 2: Indirect emissions – owned:
    • Indirect emissions from the generation of purchased energy, from a utility provider. In other words, all GHG emissions released in the atmosphere, from the consumption of purchased electricity, steam, heat and cooling. For most organisations, electricity will be the unique source of scope 2 emissions. Simply stated, the energy consumed falls into two scopes: Scope 2 covers the electricity consumed by the end-user. Scope 3 covers the energy used by the utilities during transmission and distribution (T&D losses).
  • Scope 3: Indirect emissions – not owned:
    • Indirect emissions – not included in scope 2 – that occur in the value chain of the reporting company, including both upstream and downstream emissions. In other words, emissions are linked to the company’s operations. According to the GHG protocol, scope 3 emissions are separated into 15 categories.

 

 

• Circular Economy: 
  • A circular economy is a systemic approach to economic development designed to benefit businesses, society, and the environment. In contrast to the ‘take-make-waste’ linear model, a circular economy is regenerative by design and aims to gradually decouple growth from the consumption of finite resources.
  • In such an economy, all forms of waste, such as clothes, scrap metal and obsolete electronics, are returned to the economy or used more efficiently.
  • The aim of a circular economy is hence to create a closed-loop system where waste and pollution are minimized and resources are conserved, reducing the environmental impact of production and consumption.
• Sustainability Vs. Circular Economy:
  • Circularity focuses on resource cycles, while sustainability is more broadly related to people, the planet and the economy. Circularity and sustainability stand in a long tradition of related visions, models and theories.
  • A sustainable circular economy involves designing and promoting products that last and that can be reused, repaired and remanufactured. This retains the functional value of products, rather than just recovering the energy or materials they contain and continuously making products anew.

 

 

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
  • Agentic AI:
    • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of artificial intelligence that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Physical AI provides the overarching framework for creating autonomous systems that act intelligently in real-world settings. Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy.
  • AI Factories:
    • AI Factories are specialized, high-performance computing centers designed to train, tune, and deploy artificial intelligence models at scale.
    • Companies and organizations involved in AI factory infrastructure and development include Nvidia, AWS, Microsoft, OpenAI, CoreWeave, Lambda, Nebius, Supermicro, and HPE. The European Union is also establishing AI Factories through its EuroHPC Joint Undertaking to foster regional innovation.