Tracking Corporate Emission Trends Over Time
What 13 years of EPA GHGRP data reveals about which companies are reducing emissions, which are increasing, and what drives the difference.
A single year of emissions data is a snapshot. Thirteen years of data is a narrative. The most informative metric on PlainCarbon is not a company's current emissions total but its trajectory — is it moving in the right direction, and is the pace consistent with stated climate goals?
Why Trends Matter More Than Totals
If you know only one thing about a company's emissions, the trend is more informative than the current total. A company emitting 50 million MT CO2e but declining 5% annually is on a fundamentally different path than one emitting 30 million MT but increasing 3% annually. In 10 years, the first will emit less than the second.
The EPA GHGRP's 13-year dataset (2011-2023) spans enough time to distinguish genuine structural changes from year-to-year fluctuations. It covers the shale gas boom, major coal plant retirements, two economic recessions, and the beginning of large-scale renewable energy deployment. This context makes trends on PlainCarbon genuinely meaningful.
Reading the Trend Chart on Company Pages
Each company page on PlainCarbon shows a multi-year emissions chart. Here is how to interpret it:
What it tells you: The direction and magnitude of change over 13 years. A steadily declining line indicates consistent progress. A flat line suggests stability. A rising line signals growing emissions, whether from expanded operations or increased intensity.
What it does not tell you: The cause of the trend. A decline could reflect genuine decarbonization or simply selling off high-emitting assets (which still emit — just under a different owner). An increase could mean expanding production to meet demand or declining efficiency.
How to use it: Look for the shape of the curve, not just the endpoints. A smooth, consistent decline is more credible than a single-year drop (which may be an anomaly). Compare the trend to the company's stated climate targets — do the numbers support the narrative?
Common Trend Patterns and What They Mean
After analyzing thousands of GHGRP records, several patterns emerge repeatedly:
The coal retirement curve: Power companies show steep declines when they retire coal plants and replace them with gas or renewables. These are the most dramatic trends in the dataset — some companies have cut emissions by 50% or more over the period.
The production-driven increase: Oil and gas companies that expanded operations show emissions increasing roughly in proportion to production growth. The question is whether emissions grow faster or slower than production — the intensity trend.
The acquisition spike: Companies that acquire high-emitting assets show sudden jumps. This is not increased pollution — it is accounting reassignment. Check the facility count: if it jumped alongside emissions, an acquisition likely explains the increase.
The flat line: Many industrial companies show remarkably stable emissions over 13 years. This may indicate consistent operations or — less charitably — a lack of decarbonization investment despite opportunities.
How Economic Cycles Affect Emission Trends
One important confounding factor in emission trends is the business cycle. During the 2008-2009 recession, US industrial emissions dropped sharply — not because companies invested in clean technology, but because production fell. When production recovered, emissions rebounded. Similarly, the COVID-19 pandemic caused a temporary emissions dip in 2020 that reversed in subsequent years.
This is why the GHGRP's 13-year dataset is so valuable. It spans multiple economic cycles, allowing you to distinguish structural changes (coal retirements, efficiency investments) from cyclical fluctuations (recessions, pandemic effects). A company whose emissions declined steadily through both growth and recession periods is demonstrating genuine structural decarbonization. One whose emissions track GDP is simply reflecting production volume.
What This Means for You: A Practical Framework
Step 1 — Check the multi-year chart. Visit the company page and examine the emissions trend. Note the direction, magnitude, and consistency.
Step 2 — Calculate the trend percentage. PlainCarbon shows the percentage change between the earliest and latest reporting years. A -30% over 13 years is roughly -2.6% per year compounded.
Step 3 — Compare to peers. Use our rankings or compare tool to see whether the trend is industry-typical or an outlier. A -3% annual decline in power generation is unremarkable; in chemicals, it would be notable.
Step 4 — Cross-reference with events. Major trend changes often correlate with specific events: facility closures, acquisitions, new regulations, or production changes. If a trend shifts suddenly, investigate why.
Frequently Asked Questions
How many years of emissions data does PlainCarbon have?
PlainCarbon has 13 years of EPA GHGRP data (2011-2023) for each reporting facility and company. This enables meaningful trend analysis across economic cycles, policy changes, and energy transitions.
What does a declining emissions trend mean?
A declining trend means the company or facility reported less CO2e over the measured period. Causes include coal-to-gas switching, facility closures, efficiency improvements, or reduced production. Trend direction alone does not indicate the cause — context matters.
Can emissions trends predict future performance?
Trends can indicate trajectory but cannot predict with certainty. A company steadily reducing emissions over 10 years is more likely to continue than one with volatile year-to-year swings. However, policy changes, economic conditions, and corporate strategy shifts can alter trajectories unexpectedly.
Why might a company show increasing emissions?
Increasing emissions may reflect expanded operations (new facilities, increased production), acquisition of high-emitting assets, or changes in fuel mix. Growth-driven increases are different from efficiency-driven increases — check whether the company grew revenue proportionally.
Sources: EPA GHGRP, FLIGHT Tool.
Last updated: April 2026
A worked example
Consider a household earning $75,000 per year facing an annual cost of $18,000 for the service this guide covers. Their cost-to-income ratio is 24% — below the 30% red-line that federal affordability frameworks use to flag burden. By comparison, a household at $45,000 facing the same $18,000 cost lands at 40% — well into severely-burdened territory under the same definitions.
Where to dig deeper
The methodology page documents exactly which federal series we draw from, how we weight regional differences, and the reference period for each metric. The research section publishes original analyses derived from the same underlying database — useful when you want to see year-over-year shifts or peer-jurisdiction comparisons that the per-page detail views don't surface.
| Threshold | Federal definition | Practical meaning |
|---|---|---|
| Below 7% | Affordable | Comfortable margin for unexpected expenses |
| 7-30% | Moderate burden | Manageable but constrains discretionary spending |
| Above 30% | Burdened | HUD definition — qualifies for federal subsidy programs |
| Above 50% | Severely burdened | Trade-offs with food, healthcare, savings |
Frequently asked questions
Where does this data come from?
All figures on this page derive from official federal data — primarily the U.S. Bureau of Labor Statistics, U.S. Census Bureau, U.S. Department of Health and Human Services, and U.S. Department of Labor. We cite the underlying agency and series in the methodology section. No proprietary aggregators are used.
How often are figures updated?
Each series follows its own publication cadence. We refresh our database within 30 days of each upstream release. Specific update timestamps appear in the page footer where available; the methodology page documents the cadence per data series.
Can I use this data for my own analysis?
Yes. The underlying federal data is public domain. Our presentation, calculations, and editorial commentary are licensed for individual reference. For commercial republication or large-scale data extraction, contact us at the email listed on the contact page.
What if the figures here disagree with another source?
Different sources use different methodologies, definitions, geographic boundaries, and reference periods — disagreement is normal and informative. Our methodology page documents exactly which series and reference period we use for each metric, so you can reproduce or audit the figures against the upstream agency directly.