English abstract
To achieve the goal of limiting global warming to 1.5 ◦C above preindustrial levels, net-zero
emissions targets were proposed to assist countries in planning their long-term reduction. Inverse
Data Envelopment Analysis (DEA) can be used to determine optimal input and output levels without
sacrificing the set environmental efficiency target. However, treating countries as having the same
capability to mitigate carbon emissions without considering their different developmental stages
is not only unrealistic but also inappropriate. Therefore, this study incorporates a meta-concept
into inverse DEA. This study adopts a three-stage approach. In the first stage, a meta-frontier DEA
method is adopted to assess and compare the eco-efficiency of developed and developing countries.
In the second stage, the specific super-efficiency method is adopted to rank the efficient countries
specifically focused on carbon performance. In the third stage, carbon dioxide emissions reduction
targets are proposed for the developed and developing countries separately. Then, a new meta inverse DEA method is used to allocate the emissions reduction target to the inefficient countries
in each of the specific groups. In this way, we can find the optimal CO2 reduction amount for
the inefficient countries with unchanged eco-efficiency levels. The implications of the new meta inverse DEA method proposed in this study are twofold. The method can identify how a DMU
can reduce undesirable outputs without sacrificing the set eco-efficiency target, which is especially
useful in achieving net-zero emissions since this method provides a roadmap for decision-makers
to understand how to allocate the emissions reduction targets to different units. In addition, this
method can be applied to heterogeneous groups where they are assigned to different emissions
reduction targets.
