Editing IPCC:AR6/WGIII/Chapter-5 (section)

=== 5.3.2 Technical Tools to Identify Avoid-Shift-Improve Options ===

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Service delivery systems to satisfy a variety of service needs (e.g., mobility, nutrition, thermal comfort, etc.) comprise a series of interlinked processes to convert primary resources (e.g., coal, minerals) into useable products (e.g., electricity, copper wires, lamps, light bulbs). It is useful to differentiate between conversion and processing steps ‘upstream’ of end users (mines, power plants, manufacturing facilities) and ‘downstream’, that is, those associated with end-users, including service levels, and direct well-being benefits for people ( [[#Kalt--2019|Kalt et al. 2019]] ). Illustrative examples of such resource processing systems and associated conversion losses drawn from the literature are shown in Figure 5.9, in the form of resource processing cascades for energy (direct energy conversion efficiencies ( [[#Nakićenović--1993|Nakićenović et al. 1993]] ; [[#De%20Stercke--2014|De Stercke 2014]] )), water use in food production systems (water use efficiency and embodied water losses in food delivery and consumption ( [[#Lundqvist--2008|Lundqvist et al. 2008]] ; [[#Sadras--2011|Sadras et al. 2011]] )), and materials ( [[#Ayres--1994|Ayres and Simonis 1994]] ; [[#Fischer-Kowalski--2011|Fischer-Kowalski et al. 2011]] ), using the example of steel manufacturing, use and recycling at the global level ( [[#Allwood--2012|Allwood and Cullen 2012]] ). Invariably, conversion losses along the entire service delivery systems are substantial, ranging from 83% (water) to 86% (energy) and 87% (steel) of primary resource inputs ( [[#TWI2050--2018|TWI2050 2018]] ). In other words, only between 14 to 17% of the harnessed primary resources remain at the level of ultimate service delivery.

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'''Figure 5.9 | Resource processing steps and efficiency cascades (in percentage of primary resource inputs [vertical axis] remaining at respective steps until ultimate service delivery) for illustrative global service delivery systems for energy (panel (a), disaggregated into three sectoral service types and the aggregate total), food (panel (b), water use in agriculture and food processing, delivery and use), and materials (panel (c), example steel).''' The aggregate efficiencies of service delivery chains is with 13–17% low. Source: [[#TWI2050--2018|TWI2050 (2018)]] .

Examples of conversion losses on the supply side of resource processing systems include, for instance: for energy, electricity generation (global output/input conversion efficiency of electric plants of 45% as shown in energy balance statistics ( [[#IEA--2020b|IEA 2020b]] )); for water embodied in food, irrigation water use efficiency (some 40% ( [[#Sadras--2011|Sadras et al. 2011]] )) and calorific conversion efficiency (food calories in to food calories out) in meat production of 60% ( [[#Lundqvist--2008|Lundqvist et al. 2008]] ), or for materials, globally only 47% of primary iron ore extracted and recovered steel scrap end up as steel in purchased products, (i.e., a loss of 57%) ( [[#Allwood--2012|Allwood and Cullen 2012]] ).

A substantial part of losses happens at the end-use point and in final service delivery (where losses account for 47% to 60% of aggregate systems losses for steel and energy respectively, and 23% in the case of water embodied in food). The efficiency of service delivery ( [[#Brand-Correa--2017|Brand-Correa and Steinberger 2017]] ) has usually both a technological component (efficiency of end-use devices such as cars, light bulbs) and a behavioural component (i.e., how efficiently end-use devices are used, e.g., load factors) ( [[#Dietz--2009|Dietz et al. 2009]] ; [[#Laitner--2009|Laitner et al. 2009]] ; [[#Norton--2012|Norton 2012]] ; [[#Kane--2014|Kane and Srinivas 2014]] ; [[#Ehrhardt-Martinez--2015|Ehrhardt-Martinez 2015]] ; [[#Thaler--2015|Thaler 2015]] ; [[#Lopes--2017|Lopes et al. 2017]] ). Using the example of mobility, where service levels are usually expressed by passenger-km, service delivery efficiency is thus a function of the fuel efficiency of the vehicle and its drivetrain (typically only about 20%–25% for internal combustion engines, but close to 100% for electric motors) plus how many passengers the vehicle actually transports (load factor, typically as low as 20–25%, i.e. one passenger per vehicle that could seat four to five), that is, an aggregate end-use efficiency of between 4–6% only. Aggregated energy end-use efficiencies at the global level are estimated as low as 20% ( [[#De%20Stercke--2014|De Stercke 2014]] ), 13% for steel (recovered post-use scrap) ( [[#Allwood--2012|Allwood and Cullen 2012]] ), and some 70% for food (including distribution losses and food waste of some 30%) ( [[#Lundqvist--2008|Lundqvist et al. 2008]] ).

To harness additional gains in efficiency by shifting the focus in service delivery systems to the end user can translate into large upstream resource reductions. For each unit of improvement at the end-use point of the service delivery system (examples shown in Figure 5.9), primary resource inputs are reduced between a factor of 6 to 7 units (water, steel, energy) ( [[#TWI2050--2018|TWI2050 2018]] ). For example, reducing energy needs for final service delivery equivalent to 1 EJ, reduces primary energy needs by some 7 EJ. There is thus ''high evidence'' and ''high agreement'' in the literature that the leverage effect for improvements in end-use service delivery efficiency through behavioural, technological, and market organisational innovations is very large, ranging from a factor 6 to 7 (resource cascades) to up to a factor 10 to 20 (exergy analysis), with the highest improvement potentials at the end-user and service provisioning levels (for systemic reviews see [[#Nakićenović--1996a|Nakićenović et al. (1996a)]] , [[#Grubler--2012b|Grubler et al. (2012b)]] , and [[#Sousa--2017|Sousa et al. (2017)]] ). Also, the literature shows ''high agreement'' that current conversion efficiencies are invariably low, particularly for those components at the end-use and service-delivery back end of service provisioning systems. It also suggests that efficiencies might actually be even lower than those revealed by direct input-output resource accounting, as discussed above (Figure 5.9). Illustrative exergy efficiencies of entire national or global service delivery systems range from 2.5% (USA ( [[#Ayres--1989|Ayres 1989]] )) to 5% (OECD average ( [[#Grubler--2012b|Grubler et al. 2012b]] )) and 10% (global (Nakićenović et al., 1996)). Studies that adopt more restricted systems boundaries, either leaving out upstream resource processing/conversion or conversely end-use and service provision, show typical exergetic efficiencies between 15% (city of Geneva ( [[#Grubler--2012a|Grubler et al. 2012a]] )) to below 25% (Japan, Italy, and Brazil, albeit with incomplete systems coverage that miss important conversion losses ( [[#Nakićenović--1996b|Nakićenović et al. 1996b]] )). These findings are confirmed by more recent exergy efficiency studies that also include longitudinal time trend analysis ( [[#Cullen--2010|Cullen and Allwood 2010]] ; [[#Brockway--2014|Brockway et al. 2014]] ; [[#Serrenho--2014|Serrenho et al. 2014]] ; [[#Brockway--2015|Brockway et al. 2015]] ; [[#Guevara--2016|Guevara et al. 2016]] ). Figure 5.10 illustrates how energy demand reductions can be realised by improving the resource efficiency cascades shown in Figure 5.9.

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'''Figure 5.10 | Realisable energy efficiency improvements by region and by end-use type between 2020 and 2050 in an illustrative Low Energy Demand scenario (in EJ).''' Efficiency improvements are decomposed by respective steps in the conversion chain from primary energy to final, and useful, energy, and to service delivery, and disaggregated by region (developed and developing countries) and end-use type (buildings, transport, materials). Improvements are dominated by improved efficiency in service delivery (153 EJ) and by more efficient end-use energy conversion (134 EJ). Improvements in service efficiency in transport shown here are conservative in this scenario but could be substantially higher with the full adoption of integrated urban shared mobility schemes. Increases in energy use due to increases in service levels and system effects of transport electrification (grey bars on top of first pair in the bar charts) that counterbalance some of the efficiency improvements are also shown. Examples of options for efficiency improvements and decision involved (grey text in the chart), the relative weight of generic demand-side strategies (Avoid-Shift-Improve blue arrows), as well as prototype actors involved, are also illustrated. Data source: Figure 5.9 and [[#Grubler--2018|Grubler et al. (2018)]] .

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