AI and the future of Environment and Sustainability work: what changes, what stays human

Key facts

• Malaysia had 31% installed renewable energy capacity by December 2025, the Deputy Prime Minister said in April 2026 (Bernama report)^[1].
• The National Energy Transition Roadmap (NETR) targets 40% renewable energy by 2035 and 70% by 2050, and estimates about 310,000 green job opportunities by 2050. Government targets, not outcomes^[2].
• The ILO scores environmental protection professionals and environmental engineers at 0.38 out of 1, and environmental health inspectors at 0.24 ("not exposed"). Global generative-AI task-exposure estimates; not a Malaysian forecast; not a job-security rating^[3].
• World Economic Forum (WEF) employers put environmental engineers and renewable energy engineers among the fastest-growing jobs to 2030. Global employer expectation, published 2025^[4].

Green jobs are being created by policy and investment at the same time as AI changes how work is done. In the ILO's index, the field occupations here score lower than the data and forecasting ones. The bigger question for you may be which green route fits you, not what AI does to it.

For routes in, SPM subjects and the qualifications energy work starts from, start with Environment & Sustainability after SPM.

How to read this page

Observed means someone measured it. Projected means someone estimated the future, so we say who, when and for where. Projections are often wrong.

The scores come from the International Labour Organization (ILO). They estimate how much of an occupation's current tasks generative AI (tools that write, summarise or make images from a prompt) could potentially do, from 0 to 1^[3]. "Not exposed" is a technical label: most of the occupation's tasks scored consistently low for what generative AI can do today^[3]. It does not mean AI, drones or sensors will never change the work, and it says nothing about Malaysian employers. Exposure is not job loss either: the ILO says that depends on adoption and on whether workers get the chance to learn the tools^[3].

What is already happening (observed)

• The energy shift is measurable. Malaysia had 31% installed renewable energy capacity by December 2025 and aimed for 32% in 2026, the Deputy Prime Minister said in April 2026, as reported by Bernama^[1].
• Farm and forest work barely appears in one AI product's data. In Anthropic's February 2025 data, farming, fishing and forestry was the least represented job group, at 0.1% of Claude conversations^[5]. Anthropic usage data (Claude.ai conversations sorted into US job categories), not Malaysian demand.
• Malaysia. We did not find a current Malaysia-specific study that measures AI use in environmental or energy jobs here.

What people project, and who says so

• Malaysia's plan. The NETR targets renewable energy shares of 40% by 2035 and 70% by 2050, and estimates about 310,000 green job opportunities by 2050^[2]. These are government targets that depend on investment and policy holding steady.
• Employers in Malaysia. 54% expected energy generation, storage and distribution technologies to transform their business by 2030, against 41% globally, and 48% expected efforts to cut carbon emissions to do so, close to the global 47%. Expectation of employers in Malaysia in the WEF's global survey, published 2025^[4].
• Energy employers worldwide. Employers in energy technology and utilities said people mainly do 43% of tasks today and expected 31% by 2030. They named AI and machine learning specialists, renewable energy engineers, sustainability specialists and energy engineers as growing roles. Global employer expectation, published 2025^[4].
• Green roles. Employers put environmental engineers and renewable energy engineers among the 15 fastest-growing jobs, and expect farmworkers to add more jobs than any other role by 2030, about 35 million, driven mainly by the green transition. Global employer expectation, published 2025^[4].

Task by task

These cards are our reading of the sources for a classroom exercise, not a forecast. A practitioner may disagree, and their reasons are worth hearing.

Collect water or soil samples in the field

• What a person does: gets there, samples safely, notices what looks wrong.
• What AI might assist: logging and checking the results afterwards.
• What this source does not show: how many field jobs Malaysian agencies and firms will hire for.

Analyse pollution data and write a compliance report

• What a person does: checks the data and signs off.
• What AI might assist: spotting patterns and drafting the report.
• What this source does not show: how regulators here will treat AI-drafted reports.

Install and maintain rooftop solar

• What a person does: roof work and wiring, safely.
• What AI might assist: sizing the system and drafting the quote.
• What this source does not show: installer demand or licensing in Malaysia.

Run a water treatment or power plant

• What a person does: responds on site when something goes wrong.
• What AI might assist: spotting unusual readings.
• What this source does not show: how Malaysian plants are automating.

Forecast weather and flood risk

• What a person does: interprets the forecast and warns people in time.
• What AI might assist: much of the number work.
• What this source does not show: how Malaysian forecasting services use AI now.

Audit a building's energy, water or waste

• What a person does: walks the site and persuades people to change habits.
• What AI might assist: crunching the bills.
• What this source does not show: a score; no single ILO occupation fits this task.

New tasks that are appearing

• Checking sensor, drone and satellite data before decisions are made on it.
• Measuring and reporting carbon figures that customers and regulators ask for, with AI helping to gather the data.
• Checking that green claims in AI-written marketing are actually true.
• Maintaining the software and controls in solar, storage and smart-grid systems.

This list is our reading of the sources above, not an official one.

What stays human: hypotheses to test

These are practitioner hypotheses to test, not findings. The sources do not show that any of these tasks will stay unchanged.

• Does fieldwork in places that change with the weather still need a person on the ground?
• Who judges what a measurement means for this river, farm or building?
• Who convinces people to change how they use energy or handle waste?
• Who answers for safety on roofs and in plants?

Ask a practitioner which of these drones, sensors or AI have already changed.

What this means for you

• Combine field skills with data skills. Systems thinking, field research and data are the skills listed for this area, and many roles mix outdoor and screen work.
• For energy jobs, start with the technical qualification. Green and semiconductor jobs in Malaysia explains the routes into solar and grid work and the electrical qualifications they start from.
• Be ready to explain. Many green roles involve persuading clients, communities or managers.
• Read targets as targets. The NETR numbers are plans; check each year's progress before you count on them.
• Watch the yearly renewable energy figures, NETR updates and which green roles appear in Malaysian job adverts.

Your next step

1. On Day 2, choose the Environment and Sustainability taster (audit the resources or waste in a space) or, if your cohort runs four labs, the Science and Discovery lab. Read the Science and Discovery lab brief first.
2. During the audit, use an AI tool to estimate one number (such as energy use) and then check it against a real bill or meter reading. Note how far off it was.

About the evidence

ILO scores (a global generative-AI task-exposure estimate; not a Malaysian forecast; not a job-security rating), on a 0 to 1 scale with the ILO's band^[3]:

• Meteorologists 0.54, gradient 3
• Environmental protection professionals 0.38, minimal exposure
• Environmental engineers 0.38, minimal exposure
• Farming, forestry and fisheries advisers 0.29, not exposed
• Power production plant operators 0.28, not exposed
• Incinerator and water treatment plant operators 0.27, not exposed
• Agricultural technicians 0.26, not exposed
• Environmental and occupational health inspectors 0.24, not exposed
• Forestry technicians 0.21, not exposed
• Building electricians 0.19, not exposed

The bands run from "not exposed" and "minimal exposure" up to gradients 1 to 4, the most exposed^[3]. The ILO built its scores from a survey of 1,640 workers in Poland, rounds of discussion with international experts, and an AI assistant built on that input. Every task counts equally, and the ILO says local factors its global study does not cover can speed up or limit the change^[3]. ILO occupation groups do not map one-to-one onto Malaysian job titles, and we do not claim a direct mapping.

WEF figures are expectations from a survey of more than 1,000 employers across 55 economies, published January 2025^[4]. Anthropic figures are its own usage data, not Malaysian demand. The method behind the task cards is in Tasks, not job titles.

For facilitators

Use the task cards as a starting list for the Day 2 AI task X-ray and ask the practitioner to correct them before learners use them. Each learner supports one card with a source and challenges one assumption. Stop any conclusion that a job is "safe" or "dead": the evidence here does not support either.