I can't even fill out the registration form in 30 minutes!

At 3 a.m., Zhang Miao, an environmental professional, stared at the automatically generated environmental impact assessment report on the screen, cold sweat dripping down his back—what used to take weeks or even months to complete, AI now does in just 30 minutes.

Recently, the Hangzhou Ecological Environment Bureau's announcement of "automatically generating environmental impact assessment reports in 30 minutes + 15 minutes for report review" has caused a stir in the environmental professional community, with comments such as "AI is taking our jobs," "I'll have to beg for food after losing my job," and "I'll switch to driving a taxi"...The industry is filled with anxiety and helplessness.

Li Cheng, who works in human resources management at an environmental enterprise, told Shuiquan:

“ Combined with the current industry situation and the broader economic environment, a wave of AI replacing human labor is sweeping in, with a large portion expected to be completed before 2030. ”

In other words, some environmental professionals may only have 5 years left.

AI's replacement of human labor in the environmental industry shows a pattern of penetration "from text to practice, from the back end to the front end."

In Li Cheng's view, jobs with a high risk of replacement generally have the following characteristics: reliance on standardized processes and document generation (such as environmental impact assessments); the need to process structured data and rules (such as monitoring and analysis); highly repetitive and low-differentiation service content (such as basic consulting).

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To avoid being replaced by AI, professionals need to possess complex on-site decision-making and emergency handling capabilities, cross-field coordination and innovative solution design, and soft skills such as policy interpretation and communication with stakeholders.

Specifically:

1. Environmental Impact Assessment Report Preparation and Approval Personnel (Replacement Rate: 90%-95%)

AI has achieved full automation of the entire process, from data entry to report generation and review. By combining DeepSeek-R1 with a domain knowledge base, companies only need 5 minutes to fill in basic data to generate a compliant environmental impact assessment report. In the approval process, the AI agent can automatically identify regulatory compliance, the rationality of pollution control measures, and generate a three-color graded problem list. This type of work is highly dependent on standardized processes and text generation capabilities, and AI has an overwhelming advantage in efficiency, consistency, and cost control.

2. Environmental Monitoring Data Analyst (Replacement Rate: 70%-80%)

AI can process massive amounts of environmental monitoring data in real time (such as water quality parameters, air quality), automatically identify pollution sources, predict diffusion trends, and generate analysis reports using algorithms. For example, the "AI Soil Pollution Prevention and Control Expert" deployed in Wuxi can already complete the pre-review and Q&A of soil pollution status reports. Traditional manual analysis relies on experience and repetitive data comparison, while AI is more efficient and accurate in multi-dimensional data association, anomaly detection, and trend prediction.

3. Environmental Consulting Consultant (Replacement Rate: 60%-70%)

For standardized consulting services such as environmental impact assessment access and pollution control technology selection, AI question-and-answer systems can provide quick responses. Hangzhou's AI system has achieved "generating access opinions in 5 minutes" and can provide regulatory interpretation and technical solution recommendations based on knowledge graphs. Repetitive and low-differentiation content in this type of service will gradually be replaced by AI, but high-end consulting requiring personalized solutions still relies on human experts.

4. Environmental Engineering Design Drafter (Replacement Rate: 40%-50%)

AI can automatically generate pollution control facility design drawings based on environmental parameters and engineering requirements, and optimize solutions through reinforcement learning. Hangzhou has already achieved automatic generation of the "main environmental impacts and protection measures" section in environmental impact assessment reports, and this may extend to engineering drawing design in the future. However, unstructured problems involving complex terrain adaptability and stakeholder coordination still require human intervention.

5. Pollution Control Site Operator "Some Functions" (Replacement Rate: 20%-30%)

AI can use IoT devices to achieve automated operation and monitoring of wastewater treatment and exhaust gas purification facilities, such as predicting equipment failures and optimizing reagent addition. However, tasks requiring flexible response and physical operation, such as equipment maintenance and emergency handling, still rely on human labor. AI serves more as an auxiliary tool, replacing low-skill, repetitive jobs.

Li Cheng also said that future wastewater treatment plants will adopt an "AI-controlled + human supervision" model. Colleagues working in wastewater treatment plants must transition to high-skill positions such as equipment maintenance experts and process optimization engineers.

Jobs with characteristics such as reliance on structured data input/output (such as monitoring data entry), adherence to clear operating rules (such as equipment start/stop logic), high repetition and low differentiation (such as patrols and paperwork) are highly likely to be replaced, while jobs requiring physical operation and on-site response (such as emergency repairs), complex system coordination (such as cross-departmental resource scheduling), reliance on experience and innovation (such as process route optimization) show low replaceability.

Specifically:

1. Water Quality Monitoring Data Entry Clerk (Replacement Rate: 90%-95%)

Wastewater treatment plants need to collect dozens of water quality parameters in real time, such as COD, ammonia nitrogen, and pH value. Traditional manual recording and data organization are inefficient and prone to errors. AI can automatically collect data through IoT sensors and use natural language processing technology to generate standardized reports. For example, a wastewater treatment plant in Hangzhou has achieved full automation of the "data acquisition-analysis-archiving" process, with manual intervention rate reduced to less than 5%.

2. Equipment Operation Monitor (Replacement Rate: 80%-85%)

AI can integrate the operating data of equipment such as water pumps, aerators, and sludge dewatering machines, predict failures through machine learning, and optimize start/stop strategies. For example, the AI system deployed in a Beijing wastewater treatment plant can automatically adjust the aeration volume according to the influent flow, reducing energy consumption by 12%. Manual monitoring only needs to handle exceptions that the system cannot determine (such as sudden mechanical jams).

3. Basic Patrol Inspector (Replacement Rate: 70%-75%)

Drones and track robots equipped with visual recognition modules can replace manual routine inspections of pools and pipelines. AI can identify the thickness of scum on the pool surface, the degree of equipment rust, etc., and generate maintenance suggestions by comparing historical data. A pilot program at a wastewater treatment plant in Chongqing shows that AI inspection coverage increased from 60% to 98%, and the miss rate decreased by 90%.

4. Standardized Administrative Clerk (Replacement Rate: 60%-70%)

AI templates can automatically generate documents such as daily production reports, environmental protection ledgers, and pollutant discharge permit applications. For example, a wastewater treatment plant in Shenzhen uses large language models (such as DeepSeek-R1) to automatically fill in the "Wastewater Treatment Facility Operation Record Table," reducing the time required from 2 hours/day to 5 minutes/day. Only key data compliance needs manual review.

5. Junior Process Engineer (Partial Functions) (Substitution Rate: 50%-60%)

Based on historical operating data and real-time water quality parameters, AI can automatically optimize process parameters such as aeration time and sludge return ratio. A wastewater treatment plant in Shanghai used an AI model to reduce the dissolved oxygen concentration control error in the biological pool from ±0.5mg/L to ±0.1mg/L. However, process route innovation and handling of sudden pollution incidents still require manual decision-making.

6. Reagent Addition Operator (Substitution Rate: 40%-50%)

AI automatically calculates the dosage of PAC, PAM, and other reagents based on water quality fluctuation prediction models (such as sudden changes in influent TP) and executes the dosage with the dosing pump. A wastewater treatment plant in Nanjing achieved dynamic adjustment of carbon source addition, increasing the carbon-nitrogen ratio qualification rate from 82% to 95% and reducing reagent costs by 18%.

7. Sludge Transportation Dispatcher (Substitution Rate: 30%-40%)

AI can integrate sludge production, vehicle GPS, road conditions, etc., to automatically generate optimal transportation routes and vehicle scheduling plans. For example, after introducing an AI scheduling system, a wastewater treatment plant in Guangzhou reduced the empty driving rate of sludge transportation vehicles from 35% to 12%. However, manual intervention is still needed in case of special weather or traffic control.

8. Emergency Response Coordinator (Substitution Rate: <10%)

Emergency situations such as heavy rain causing inflow exceeding standards and sudden equipment failures require rapid coordination of multiple resources, involving cross-departmental communication and risk assessment, which are unstructured tasks. Currently, AI can only provide auxiliary decision support (such as retrieving historical case libraries).

Undoubtedly, current environmental protection professionals must have the awareness of transforming into "AI collaborators." The best approach to this massive change is to "join if you can't beat it."

Finally, to conclude, I'll borrow a quote from Jensen Huang of NVIDIA—AI will never replace environmental protection professionals, but environmental protection professionals who don't use AI will be replaced by those who do.