Navigating the Baseline: A Guide to Environmental Assessment and Decision-Making

Baselines serve as the bedrock for better outcomes. No, not tennis court lines, but the foundation for environmental assessment and decision making.

Before diving further, lets define a few terms

Baselines are the starting point against which environmental indicators, such as GHG emissions are measured. One legal definition describes them as “… the environmental conditions found in habitats, ecosystems, elements and natural resources, as well as interaction relations and environmental services present in the Contract Area prior to the execution of the contractual activities.” They represent the current state of a particular aspect of the environment, such as emissions, air quality, water quality, biodiversity, or other environmental parameters. The purpose of baselining is to kickstart an evaluation of how different activities, projects, or policies shape the environment.

A benchmark is a standard, reference point, or target used for comparison and evaluation. It represents a predefined level of environmental improvement that a project aims to achieve. By comparing the project’s achieved outcomes to the benchmark and using the baseline as the starting point, the effectiveness of the project can be determined.

Difference between baselines and benchmarks: benchmarks are standards or targets against which performance or quality is measured, while baselines are initial reference points used to measure change or impact over time.

A project standard outlines the overall framework within which a specific type of project can generate environmental credits. It sets the rules, methodologies, and criteria that a project must adhere to in order to qualify for credit issuance. Project standards are typically developed for specific project types, such as afforestation, reforestation, renewable energy installations, etc. As part of the project standard, a baseline is established to represent the initial conditions of the environment, including indicators like GHG emissions, air quality, water quality, or biodiversity. This baseline serves as the starting point against which the project’s performance and impacts are measured. The project’s success in achieving the defined benchmarks, which are established within the performance standard, is evaluated by comparing the project’s outcomes to the baseline.

A performance standard focuses on setting specific criteria and thresholds that a project must achieve in order to generate a certain amount of credits. Performance standards are usually more detailed and quantifiable, defining the specific environmental outcomes that a project needs to deliver to be eligible for credits. These outcomes are often linked to predetermined benchmarks, which act as reference points for assessing the project’s success.

Difference between project standards and performance standards: A project standard outlines the overarching framework and criteria for generating environmental credits in a specific project type, while a performance standard sets specific measurable criteria and thresholds that a project must achieve to qualify for a certain amount of credits.

Examples that tie it all together

Example 1: A farmer is currently intensively tilling, over-applying nitrogen, and mono-cropping. This would set a business as usual baseline that could be compared against the results from adopting a no-till system, a more complex crop rotation, reducing input usage, and applying cover crops. In a carbon offset program, a project standard would reward the farmer for any additional carbon reductions and removals that resulted from the new practices against a baseline from the business as usual practices.

Example 2: Low Carbon Fuel Standard (LCFS) can be seen as a combination of both performance and project standards. It establishes performance standards in the form of carbon intensity targets that fuels must meet. These performance standards are set based on baseline values and seek to achieve quantifiable emissions reductions. However, the LCFS also indirectly involves elements of project standards. Fuel producers can engage in specific projects or practices that reduce carbon intensity, such as incorporating biofuels or implementing other clean technologies. These projects contribute to meeting the performance standards set by the LCFS. So, while the primary focus is on performance standards, the programs and practices that fuel producers undertake to achieve those standards could be seen as aligning with project standard principles.

Example 3: A farmer commits to adopting precision agriculture techniques that precisely deliver nitrogen fertilizers to the crop based on real-time data about soil conditions, weather, and crop needs. The performance standard might specify that the farmer must achieve a certain percentage reduction in nitrogen fertilizer use compared to conventional practices while ensuring that the crop yield remains consistent or even improves. The assessment of whether the performance standard is met would involve comparing the farmer’s actual nitrogen fertilizer usage and crop yield data to the defined targets. If the farmer successfully reduces nitrogen fertilizer use by the specified percentage without negatively impacting crop yield, they would be eligible for environmental credits or incentives.

How to set a baseline

Baselining involves quantifying the amount of emissions being reduced or produced by a certain entity or activity at a specific point in time. This baseline data is then used to compare against future emissions data, allowing for an assessment of whether emissions have increased, decreased, or remained stable and often fitting into crediting schemes. Generally speaking, the process of baselining follows these 8 steps.

1. Define Objectives: Clarify the goals and objectives of the baseline assessment. Are you measuring progress, compliance, participating in a voluntary incentive scheme, or evaluating an initiative’s impact?
2. Select Indicators: Choose relevant parameters and indicators that align with your objectives. These should provide a comprehensive understanding of the situation.
3. Collect Data: Gather data for the selected indicators using reliable methods and sources.
4. Validate and Quality Check: Ensure data accuracy and quality through validation and quality control processes. 3rd party verify data if required.
5. Calculate Baseline Values: Calculate baseline values for the selected indicators, representing the current state or condition.
6. Contextual Consideration: Account for contextual factors, such as natural variability and external influences, that might impact the baseline.
7. Stakeholder Engagement: Involve stakeholders to gain insights, validate findings, and ensure a well-rounded assessment / feedback loop.
8. Communicate Results: Present the baseline results effectively, whether it’s for internal decision-making, stakeholder engagement, or regulatory reporting.

Example 1: NRCS funding

1. Define Objectives: A farmer, with support from NRCS funding, aims to assess the effectiveness of implementing conservation practices to reduce soil erosion and improve water quality in a specific field.
2. Select Indicators: They choose indicators such as soil erosion rates, sediment runoff, and water quality parameters (e.g., nutrient levels) to evaluate the success of the conservation measures.
3. Collect Data: The farmer collects data on soil erosion through erosion pins and sediment traps, measures water quality using regular sampling, and tracks conservation practice implementation.
4. Validate and Quality Check: They follow NRCS guidelines for data collection, involve NRCS field staff for validation, and ensure consistent monitoring.
5. Calculate Baseline Values: Using historical erosion rates and water quality data, they establish baseline values for soil erosion, sediment runoff, and water quality parameters before implementing conservation practices.
6. Contextual Consideration: They consider weather patterns, field topography, and other factors that could influence soil erosion and water quality outcomes.
7. Stakeholder Engagement: They work closely with NRCS staff, local watershed organizations, and other farmers to gain insights and validate findings.
8. Communicate Results: The farmer shares the baseline results with NRCS, neighboring farmers, and local community members to demonstrate the benefits of conservation practices and the positive impact on soil and water quality and comply with funding criteria.

Example 2: LCFS market

1. Define Objectives: A group of farmers participating in a sustainable agriculture program aims to assess the carbon intensity reduction achieved by producing feedstock for biofuels compared to conventional crops. They seek to access LCFS credits for their sustainable practices.
2. Select Indicators: They choose indicators such as carbon sequestration in soil, emissions from fertilizer application, and land-use changes to evaluate the environmental benefits of their sustainable feedstock production.
3. Collect Data: The farmers collect data on soil carbon content through soil sampling, measure fertilizer application rates and emissions through record-keeping, and track land-use changes over time.
4. Validate and Quality Check: They collaborate with agricultural experts to ensure accurate soil sampling and measurement of emissions, following best practices and data quality standards.
5. Calculate Baseline Values: Using data from conventional crop production methods, and LCA databases, they establish baseline values for emissions from fertilizer application and carbon sequestration in soil.
6. Contextual Consideration: They account for variations in soil types, weather patterns, and regional farming practices that might influence soil carbon sequestration and emissions.
7. Stakeholder Engagement: The farmers engage with agricultural extension services, soil scientists, and sustainability organizations to validate their data and approach.
8. Communicate Results: The group of farmers presents their baseline results to their cooperative, biofuel producers, and relevant LCFS regulatory authorities to demonstrate the carbon intensity reduction achieved through sustainable feedstock production and sell LCFS credits.

Example 3: Carbon credit program

1. Define Objectives: A farmer decides to participate in the VM-42 Verra Methodology to sell carbon credits generated through improved land management practices. The objective is to quantify the carbon sequestration achieved by adopting sustainable agricultural practices.
2. Select Indicators: The farmer selects indicators such as baseline soil carbon levels, models of soil organic carbon stock change, adoption of new practices, reduced soil disturbance, fertilizer management and erosion control to assess the impact of their sustainable practices.
3. Collect Data: The farmer collects data on soil carbon through regular soil sampling, tracks crop yields through harvest records, and measures fertilizer application rates and emissions using established methods, such as Daycent-COMET farm.
4. Validate and Quality Check: They adhere to VM-42 protocol guidelines for data collection and quality control, ensuring data accuracy and reliability and undergo 3rd party validation.
5. Calculate Baseline Values: Using historical soil carbon levels and practices, they establish baseline values for carbon sequestration potential and crop productivity under conventional practices.
6. Contextual Consideration: The farmer accounts for variations in soil types, weather patterns, and specific crop types that might influence carbon sequestration and yield improvements.
7. Stakeholder Engagement: The farmer collaborates with agricultural experts, extension services, and carbon credit verifiers to validate their data and ensure compliance with VM-42 requirements.
8. Communicate Results: The farmer presents their baseline results to the carbon credit verifier, Verra, and potential buyers of carbon credits to demonstrate the carbon sequestration achieved through their sustainable agricultural practices, aligning with VM-42 protocol standards.

Issues with baselining

Baselining is often more of an art than a science. It has a broad degree of subjectivity of how it is done across different programs, and even when done “scientifically”, there are quite a few issues with it:

• Inappropriate Reference Period: If the baseline data is collected during a period of unusual environmental conditions (e.g., an abnormally high or low pollution level), it can skew the comparisons and lead to incorrect assessments of the impact of interventions.
• Inappropriate use of metrics associated with guidance: Baselines can be set with course data that grossly misrepresent the environmental conditions when more accurate methods are available.
• Disadvantaging innovation: New methods that may benefit environmental activity do not have equitable access to informing the processes of how baselines are set and improve.
• Changing Conditions: In rapidly changing environments, the baseline data might become outdated and not reflective of the current situation. For instance, in areas experiencing significant urban development, the baseline data might no longer accurately represent the environment due to changes in land use and emissions sources.
• Lack of Context: If the baseline data is collected without considering the broader context of the environment, or metadata, it might not account for natural variations or underlying trends. This could result in misinterpretation of the effects of interventions.
• Single Metric Focus: Relying solely on a single baseline metric can lead to an incomplete understanding of the environmental situation. For instance, focusing solely on greenhouse gas emissions without considering other pollutants or ecological factors might miss important nuances.
• Neglecting Cumulative Effects: Some projects or interventions might have cumulative effects that cannot be fully captured by simply comparing against a single baseline. For instance, a series of small changes might individually seem insignificant but collectively have a significant impact.
• Ignoring Feedback Loops: Some interventions might trigger feedback loops that influence the environment over time. If these feedback loops aren’t considered, the baseline might not adequately predict the long-term effects. A key feedback loop is to ensure the utility and accuracy of scenarios against business activities, the absence of which creates siloed activities.
• Complex Systems: In complex ecosystems, the baseline might not account for all the interconnections and dependencies. Changes in one area could lead to unexpected consequences in another.
• Changing Goals: If the goals of an intervention change over time, the initial baseline might not align with the new objectives. This could lead to confusion when evaluating the success of the intervention.
• Data Quality Issues: If the baseline data is collected using inaccurate or inconsistent methods, the subsequent comparisons will also be flawed.
• Short-Term Focus: Some interventions might have short-term impacts that are not fully captured by baseline data. Over time, these impacts might diminish or become more pronounced.

How to overcome these issues by using baselines

If you are creating your own baseline, and feeling overwhelmed, or like this is just a worthless endeavor of doctoring numbers with incorrect assumptions, not all is lost! Do these things and you’re on your way to addressing the above issues:

• Contextual Understanding: Ensure a deep understanding of the environmental system you’re assessing. Develop a complete understanding of what operational data is already collected. Consider natural variations, trends, and any ongoing changes that might impact the baseline data. Determine an understanding of impacts on baselines and their ability to affect policy, regulation, economics, markets, and social influences.
• Long-Term Monitoring: Implement long-term monitoring programs that capture changes over time. This can help account for seasonal variations, trends, and potential shifts in the environment.
• Multiple Metrics: Instead of relying solely on a single metric, consider a range of relevant indicators that collectively provide a more comprehensive view of the environment’s health.
• Quality Data Collection: Ensure that the data collected for the baseline is accurate, consistent, and collected using reliable methods. Improving data collection processes will allow for making meaningful comparisons and improvements.
• Scale Appropriate Data Collection: Understand and utilize multiple approaches and scales (small plot, strip trial, community, landscape) for data collection to understand inference space and generalizability.
• Transparency and Documentation: Clearly document the methods, assumptions, and data sources used in establishing the baseline. This transparency ensures that others can understand and critique your approach. Take a lead in the industry and share your approach for feedback!
• Scenario Analysis: Use scenario analysis to assess the potential impacts of different interventions on the environment and integrate with operational decision making. Link this to meaningful budgeting questions. This can help account for complex interactions and feedback loops.
• Power Analysis: Use a power analysis that could to understand the statistical power associated with effects expected and sample sizes.
• Sensitivity Analysis: Conduct sensitivity analyses to understand how changes in key assumptions impact the results. This helps identify the most influential factors and potential sources of uncertainty.
• Dynamic Baseline: Recognize that the baseline might need to be updated periodically to reflect changing conditions. This is particularly important in rapidly evolving environments.
• Holistic Approach: Consider the broader ecological, social, and economic context when evaluating the impact of interventions. This prevents tunnel vision on a single aspect of the environment. This also directly links environmental outcomes to the management decisions.
• Stakeholder Engagement: Involve relevant stakeholders, including local communities, experts, and organizations, in the baseline process. Their insights can provide valuable context and help identify potential issues.
• Adaptive Management: Embrace adaptive management principles, which involve a flexible and iterative approach to decision-making. Adjust strategies based on ongoing monitoring and feedback.
• Constant Learning: Approach baselining as an ongoing learning process. Each assessment provides insights that can improve future assessments.

While there are many flaws in how they are applied today, baselines are the unsung heroes of decision-making. They anchor our understanding of change by capturing the present state of our environment, providing a measuring stick for the effects of actions, projects, and policies. In essence, baselines offer a canvas for progress, a compass for navigating uncharted territories, and a gauge for the success of our endeavors.

Importantly, they also address skepticism towards environmental, social, and governance (ESG) practices. Through transparency, setting and sharing baselines invites scrutiny and evidence-based evaluation that should lead to more trust and system wide improvement. Baselines provide tangible evidence that intentions align with actions. They stand as an antidote to skepticism, bridging the gap between aspirations and reality, and building trust.

Let’s keep setting baselines, not just as markers of change, but as pillars of transparency. They symbolize our commitment to a responsible, sustainable future — a future grounded in truth, measurement, and quantifiable progress. Remember, stakeholder engagement is a key part. If you have already documented your baselines and are looking to engage a stakeholder, or are seeking to do this in a more FAIR (Findable, Accessible, Interoperable, Repeatable) way, please get in touch!