107
is estimated at 236.76 and 197.16 Mg C/ha, respectively (99% is soil organic carbon)
(Sathiyamohan, 2021).
Moreover, it could be complex to model the carbon sequestration process in a limited region
(administrative boundaries), leading to the question: where should this sequestration land be
located? Especially for carbon emissions generated by transportation, as transportation
activity remains the major source of GHG emissions in developing countries (NabaviPelesaraei et al., 2017). Another comment could be attributed to the interpretation of this EF
component, as the equivalent required land to sequester carbon estimate is based on the
sequestration capacity of forests, which differs as a function of climate (e.g., temperate,
boreal). It is also worth noting that these ecosystems are also associated with other services,
such as providing wood for heat and construction. In this case, we would precisely argue the
EF's effectiveness in depicting several demands on one productive area. It is also the case for
cropland, which serves as the provider of a resource (crops, animal feed), habitats (soil
biodiversity), and human settlement lands (infrastructures). However, the pressure exerted
on these bioproductive areas is poorly illustrated by the EF. Indeed, other than changes due
to reducing physical hectares (e.g., bushfires), their regenerative capacity seems "unalterable"
over the years, neglecting the state of these lands (Van den Berg et al., 2013) and the used
technologies (Lenzen & Murray, 2001). This oversimplification in the productive lands'
biological capacity assessment is primarily due to the use of the FAO’s Global Agro-Ecological
Zoning model, which considers five land categories index based on potential crop productivity
(Borucke et al., 2013; Wackernagel et al., 2020). Some authors introduced the net primary
production appropriation to improve this accounting issue, particularly the demand for fishery
products (Talbert et al., 2006) and grazing land (refined NFA, since 2008). Consequently,
accounting for the amount of available biomass required to feed the "consumable"
biodiversity or to produce animal meat and related products (e.g., milk) provides the EF the
ability to measure humans' share of resources and, indirectly, its reliance on this biodiversity.
Furthermore, this estimate is relevant as it sets thresholds (similar to the maximum
sustainable yield) expressed in primary production (De Leo, 2013; Akrour & Grimes, 2022).
The latter is better illustrated at the local level, avoiding aggregated data related to trade.
Whether the wilaya or the national case, both models are data-intensive and have advantages
and shortcomings (Simmons & Wackernagel, 2000; Wiebe et al., 2016). The bottom-up model,
or the component footprint, requires LCA output data, especially for the embodied energy in
production processes (fertilizers, consumables) and operational energy. Indeed, LCA
approaches are advantageous, as they provide an accurate description of the upstream supply
chain emission to build a carbon inventory (carbon footprint) (Swiader et al., 2018), which
could then be converted into the equivalent offset area (ecological footprint of carbon).
Therefore, the model's limitations are those of the LCA: the definition of the study boundaries
(cradle to the gate or cradle to grave), the LCA databases used, and the proxy in case of data
deficiency. Indeed, the component approach requires adjustment to the study area, data
availability, and the use of proxies (El Bouazzaoui et al., 2007). Nevertheless, even the LCA
studies are poorly documented in Algeria or recent and cover a specific activity.
Therefore, LCA should be refined to avoid over or under-estimating CO2 emissions, thus, a
is estimated at 236.76 and 197.16 Mg C/ha, respectively (99% is soil organic carbon)
(Sathiyamohan, 2021).
Moreover, it could be complex to model the carbon sequestration process in a limited region
(administrative boundaries), leading to the question: where should this sequestration land be
located? Especially for carbon emissions generated by transportation, as transportation
activity remains the major source of GHG emissions in developing countries (NabaviPelesaraei et al., 2017). Another comment could be attributed to the interpretation of this EF
component, as the equivalent required land to sequester carbon estimate is based on the
sequestration capacity of forests, which differs as a function of climate (e.g., temperate,
boreal). It is also worth noting that these ecosystems are also associated with other services,
such as providing wood for heat and construction. In this case, we would precisely argue the
EF's effectiveness in depicting several demands on one productive area. It is also the case for
cropland, which serves as the provider of a resource (crops, animal feed), habitats (soil
biodiversity), and human settlement lands (infrastructures). However, the pressure exerted
on these bioproductive areas is poorly illustrated by the EF. Indeed, other than changes due
to reducing physical hectares (e.g., bushfires), their regenerative capacity seems "unalterable"
over the years, neglecting the state of these lands (Van den Berg et al., 2013) and the used
technologies (Lenzen & Murray, 2001). This oversimplification in the productive lands'
biological capacity assessment is primarily due to the use of the FAO’s Global Agro-Ecological
Zoning model, which considers five land categories index based on potential crop productivity
(Borucke et al., 2013; Wackernagel et al., 2020). Some authors introduced the net primary
production appropriation to improve this accounting issue, particularly the demand for fishery
products (Talbert et al., 2006) and grazing land (refined NFA, since 2008). Consequently,
accounting for the amount of available biomass required to feed the "consumable"
biodiversity or to produce animal meat and related products (e.g., milk) provides the EF the
ability to measure humans' share of resources and, indirectly, its reliance on this biodiversity.
Furthermore, this estimate is relevant as it sets thresholds (similar to the maximum
sustainable yield) expressed in primary production (De Leo, 2013; Akrour & Grimes, 2022).
The latter is better illustrated at the local level, avoiding aggregated data related to trade.
Whether the wilaya or the national case, both models are data-intensive and have advantages
and shortcomings (Simmons & Wackernagel, 2000; Wiebe et al., 2016). The bottom-up model,
or the component footprint, requires LCA output data, especially for the embodied energy in
production processes (fertilizers, consumables) and operational energy. Indeed, LCA
approaches are advantageous, as they provide an accurate description of the upstream supply
chain emission to build a carbon inventory (carbon footprint) (Swiader et al., 2018), which
could then be converted into the equivalent offset area (ecological footprint of carbon).
Therefore, the model's limitations are those of the LCA: the definition of the study boundaries
(cradle to the gate or cradle to grave), the LCA databases used, and the proxy in case of data
deficiency. Indeed, the component approach requires adjustment to the study area, data
availability, and the use of proxies (El Bouazzaoui et al., 2007). Nevertheless, even the LCA
studies are poorly documented in Algeria or recent and cover a specific activity.
Therefore, LCA should be refined to avoid over or under-estimating CO2 emissions, thus, a