According to [6, 19], many of the PB control variables differ from the indicators
of the conventional ESAMs (including LCA), particularly with respect to the point
of impact assessment although these indicators evaluate similar kinds of environmental impacts to those reported in the PBs. Hence, the chosen SDG indicators and
the PB control variables are further classified into driver, pressure, state, impact and
response indicators using an DPSIR impact pathway framework. Having classified
them, the interlinkages between the SDGs and PBs are explored. This enables
subsequent development of a complementary set of global boundaries for the SDG
indicators using, where appropriate, the thresholds proposed for the control variables in the PBs. Afterwards, the global boundaries can be allocated to lower
economic levels using a top-down approach. The method is operationalised by
developing distance-to-target measurements based on the calculated environmental
boundaries compared with the current environmental performance of the systems
under analysis (calculated using conventional ESAMs like LCA and environmental
footprint studies). These distance-to-target measurements could be positive or
negative depending on whether the system is performing in line with the goals and
targets reported under the SDGs.
4 Linkages Between the SDGs and PBs
This section details how the PBs can be employed as a complementary set of global
boundaries for the SDGs by providing a systematic comparison between the SDG
indicators and the PB control variables. Firstly, the SDG indicators concerned with
the AoPs of human health, ecosystem quality, resource scarcity and man-made
environment were chosen (a total of 73 indicators). This set of SDG indicators
comprised all the SDG indicators under the SDGs for clean water and sanitation
(SDG 6), responsible consumption and production (SDG 12), climate action (SDG
13), life below water (SDG 14) and life on land (SDG 15), plus CO 2 emission per
unit of value added (SDG Indicator 9.4.1), economic loss due to natural disasters
(SDG Indicator 1.5.2), levels of fine particulate matter in cities (SDG Indicator
11.6.2), and proportion of land for sustainable agriculture (SDG Indicator 2.4.1).
Then, as outlined in Sect. 3, the chosen SDG indicators were mapped onto a
network of cause-effect chains (developed based on the environmental problems
addressed in the SDGs and PBs) along with the PB control variables and linked
together wherever relevant (see Fig. 1). This mapping step further classified the
SDG indicators into driver (0 SDG indicators), pressure (2 SDG indicators), state
(19 SDG indicators), impact (14 SDG indicators) and response (38 SDG indicators)
indicator categories.
As emphasised in [1, 2], human societies should be operating within the
thresholds reported in the PBs. Therefore, this section focuses on the PBs and
discusses how each PB (shown in bold text) is related to different SDGs. Steffen
et al. introduced a PB called “freshwater use” and two control variables to evaluate
the challenges resulting from absolute water withdrawals [2]. The proposed control
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of the conventional ESAMs (including LCA), particularly with respect to the point
of impact assessment although these indicators evaluate similar kinds of environmental impacts to those reported in the PBs. Hence, the chosen SDG indicators and
the PB control variables are further classified into driver, pressure, state, impact and
response indicators using an DPSIR impact pathway framework. Having classified
them, the interlinkages between the SDGs and PBs are explored. This enables
subsequent development of a complementary set of global boundaries for the SDG
indicators using, where appropriate, the thresholds proposed for the control variables in the PBs. Afterwards, the global boundaries can be allocated to lower
economic levels using a top-down approach. The method is operationalised by
developing distance-to-target measurements based on the calculated environmental
boundaries compared with the current environmental performance of the systems
under analysis (calculated using conventional ESAMs like LCA and environmental
footprint studies). These distance-to-target measurements could be positive or
negative depending on whether the system is performing in line with the goals and
targets reported under the SDGs.
4 Linkages Between the SDGs and PBs
This section details how the PBs can be employed as a complementary set of global
boundaries for the SDGs by providing a systematic comparison between the SDG
indicators and the PB control variables. Firstly, the SDG indicators concerned with
the AoPs of human health, ecosystem quality, resource scarcity and man-made
environment were chosen (a total of 73 indicators). This set of SDG indicators
comprised all the SDG indicators under the SDGs for clean water and sanitation
(SDG 6), responsible consumption and production (SDG 12), climate action (SDG
13), life below water (SDG 14) and life on land (SDG 15), plus CO 2 emission per
unit of value added (SDG Indicator 9.4.1), economic loss due to natural disasters
(SDG Indicator 1.5.2), levels of fine particulate matter in cities (SDG Indicator
11.6.2), and proportion of land for sustainable agriculture (SDG Indicator 2.4.1).
Then, as outlined in Sect. 3, the chosen SDG indicators were mapped onto a
network of cause-effect chains (developed based on the environmental problems
addressed in the SDGs and PBs) along with the PB control variables and linked
together wherever relevant (see Fig. 1). This mapping step further classified the
SDG indicators into driver (0 SDG indicators), pressure (2 SDG indicators), state
(19 SDG indicators), impact (14 SDG indicators) and response (38 SDG indicators)
indicator categories.
As emphasised in [1, 2], human societies should be operating within the
thresholds reported in the PBs. Therefore, this section focuses on the PBs and
discusses how each PB (shown in bold text) is related to different SDGs. Steffen
et al. introduced a PB called “freshwater use” and two control variables to evaluate
the challenges resulting from absolute water withdrawals [2]. The proposed control
416
C. Chandrakumar and S. J. McLaren