Iron and Steel Targets under CCTS need a further look
India's Carbon Credit Trading Scheme sets each obligated plant a greenhouse gas intensity target: a limit on how much carbon it may emit per unit of output, which for almost every plant sits below its own baseline and tightens over time. Among the 255 iron and steel entities in the scheme, 41 are an exception. Their 2026-27 targets sit above their own 2023 baseline intensities. On paper, these plants are permitted to emit more carbon per tonne than they did in the baseline year, not less.
Individually these are small deviations, and in aggregate they are minor against the scheme as a whole. But a target above a plant's own baseline is not something a straightforward intensity-reduction rule produces. If each plant's target were simply its baseline minus a required cut, a target above baseline could not arise. These 41 cases therefore point to something specific in how steel targets were set.
The most plausible explanation is that steel targets were set against a wider reference than each plant's own baseline: a benchmark for a category or production route. India's steel sector spans very different technologies, from integrated blast furnaces to sponge iron and induction furnace units, with genuinely different emission intensities. If targets were anchored to a route-level or category-level benchmark rather than to each plant's individual number, a plant already performing better than that benchmark could be assigned a target at the benchmark level, which would land above its own baseline. This is a characteristic feature of benchmark-based or output-based target setting, in which efficient plants are given headroom toward a common standard rather than pushed further below it.
Two features of the data are consistent with that reading. Every one of the 41 sits below the median baseline intensity of the other obligated steel plants in its own district, in several cases far below: one Raipur plant reports a baseline of 0.88 against a district median of 2.70. And they cluster geographically, with 15 of the 41 in Chhattisgarh, concentrated in the Raipur and Durg belt. Plants that happened to report an unusually clean baseline year would be scattered across the sector, not grouped in one region and sitting consistently at the efficient end of their local peer group. The pattern looks systematic, which points to how this part of the sector was benchmarked rather than to 41 unrelated cases.
What the available data cannot resolve is which reference was used, and why. The scheme's public notifications set out the targets but not the methodology behind them: whether steel was benchmarked by production route, against what reference intensity, and how individual baselines were treated. The table below lists all 41 so the pattern can be examined directly.