Editing IPCC:AR6/WGIII/Chapter-11 (section)

=== 11.2.1 Major Drivers ===

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The use of materials is deeply coupled with economic development and growth. For centuries, humanity has been producing and using hundreds of materials ( [[#Ashby--2012|Ashby 2012]] ), the diversity of which skyrocketed in the recent half-century to achieve the desired performance and functionality of multiple products (density; hardness; compressive strength; melting point, resistance to mechanical and thermal shocks and to corrosion; transparency; heat- or electricity conductivity; chemical neutrality or activity, to name a few). New functions drive the growth of material complexity of products; for example, a modern computer chip embodies over 60 different elements ( [[#Graedel--2015|Graedel et al. 2015]] ).

Key factors driving up industrial GHG emissions since 1900 include population and per capita GDP, [[#footnote-025|2]] while energy efficiency and non-combustion GHG emissions intensity (from industrial processes and waste) has been pushing it down. Material efficiency factors – material stock intensity of GDP and ratio of extraction, processing and recycling of materials per unit of built capital along with combustion-related emissions intensity factors and electrification – were cyclically switching their contributions with relatively limited overall impact. Growing recycling allowed for replacement of some energy-intensive virgin materials and thus contributed to mitigation. In 2014–2019, a combination of these drivers allowed for a slowdown in the growth of industrial GHG emissions to below 1% (Figure 11.2 and Table 11.1), while to match a net zero emissions trajectory it should decline by 2% yr –1 in 2020–2030 and by 8.9% yr –1 in 2030–2050 ( [[#IEA--2021a|IEA 2021a]] ).

'''Table 11.1 | Dynamics and structure of industrial greenhouse gas (''' '''GHG) emissions.'''

{| class="wikitable"
|-
! rowspan="2"|
! colspan="4"| Average annual growth rates

! colspan="5"| Share in total industrial sector emissions

! rowspan="2"| 2019 emissions MtCO 2 -eq

|-
! 1971–1990

! 1991–2000

! 2000–2010

! 2011–2019

! 1970

! 1990

! 2000

! 2010

! 2019

|-
| rowspan="8"| Direct CO 2 emissions from fuel combustion

| Mining (excl. fuels), manufacturing industries and construction

| 0.13%

| –0.18%

| 4.62%

| 0.77%

| 45.8%

| 37.3%

| 33.2%

| 36.6%

| 34.9%

| 6981

|-
| Iron and steel

| 0.20%

| 0.13%

| 5.62%

| 2.28%

| 12.4%

| 10.2%

| 9.4%

| 11.4%

| 12.4%

| 2481

|-
| Chemical and petrochemical

| 3.66%

| 1.54%

| 3.16%

| 1.19%

| 3.0%

| 4.9%

| 5.2%

| 4.9%

| 4.9%

| 977

|-
| Non-ferrous metals

| 2.12%

| 3.20%

| 1.12%

| 1.36%

| 0.7%

| 0.8%

| 1.0%

| 0.8%

| 0.8%

| 163

|-
| Non-metallic minerals

| 2.91%

| 1.88%

| 6.24%

| –0.04%

| 3.3%

| 4.6%

| 5.0%

| 6.5%

| 5.7%

| 1148

|-
| Paper, pulp and printing

| 0.78%

| 2.79%

| 0.09%

| –2.69%

| 1.4%

| 1.3%

| 1.5%

| 1.1%

| 0.7%

| 150

|-
| Food and tobacco

| 2.55%

| 1.50%

| 3.03%

| –1.04%

| 1.3%

| 1.6%

| 1.7%

| 1.6%

| 1.3%

| 265

|-
| Other

| –1.55%

| –2.89%

| 4.61%

| –0.22%

| 23.8%

| 13.8%

| 9.4%

| 10.3%

| 9.0%

| 1797

|-
| colspan="2"| Indirect emissions – electricity

| 2.87%

| 2.06%

| 3.00%

| –0.87%

| 17.6%

| 24.6%

| 27.3%

| 25.8%

| 21.2%

| 4236

|-
| colspan="2"| Indirect emissions – heat

| 2.08%

| –3.09%

| 2.53%

| 9.83%

| 5.6%

| 6.7%

| 4.5%

| 4.0%

| 8.3%

| 1663

|-
| rowspan="5"| Industrial processes CO 2

| Total

| 1.45%

| 2.16%

| 5.00%

| 1.93%

| 11.0%

| 11.6%

| 13.0%

| 14.9%

| 15.7%

| 3144

|-
| Non-metallic minerals

| 2.22%

| 2.36%

| 5.66%

| 1.67%

| 5.7%

| 7.0%

| 8.0%

| 9.7%

| 10.0%

| 2008

|-
| Chemical and petrochemical

| 4.51%

| 2.52%

| 3.50%

| 2.01%

| 1.5%

| 2.9%

| 3.4%

| 3.4%

| 3.6%

| 720

|-
| Metallurgy

| –3.11%

| 0.37%

| 5.16%

| 3.10%

| 3.6%

| 1.5%

| 1.4%

| 1.7%

| 2.0%

| 391

|-
| Other

| 1.55%

| 2.30%

| –1.21%

| 2.89%

| 0.1%

| 0.2%

| 0.2%

| 0.1%

| 0.1%

| 25

|-
| colspan="2"| Industrial product use GHG

| –0.22%

| –0.49%

| –1.02%

| 0.41%

| 2.7%

| 2.0%

| 1.7%

| 1.1%

| 1.0%

| 204

|-
| colspan="2"| Other non-CO 2 GHG

| –0.60%

| 5.20%

| 4.29%

| 3.20%

| 5.5%

| 3.9%

| 5.8%

| 6.2%

| 7.3%

| 1470

|-
| colspan="2"| Waste GHG

| 1.94%

| 1.35%

| 1.22%

| 1.57%

| 11.9%

| 13.8%

| 14.4%

| 11.4%

| 11.6%

| 2327

|-
| colspan="2"| Total GHG

| 1.16%

| 0.98%

| 3.61%

| 1.32%

| 100.0%

| 100.0%

| 100.0%

| 100.0%

| 100.0%

| 20,025

|}

Source: calculated based on [[#Crippa--2021|Crippa et al. (2021)]] ; [[#IEA--2021b|IEA (2021b)]] ; and [[#Minx--2021|Minx et al. (2021)]] .

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'''Figure 11.2 | Average annual growth rates of industrial sector GHG emissions and drivers (''' '''1900–2019''' ''').''' '''Before 1970, GHG emission (other) is limited to that from cement production. Waste emission is excluded. Primary material extraction excludes fuels and biomass. Presented factors correspond directly to Equation 11.1.''' Sources: population before 1950 and GDP before 1960: [[#Maddison%20Project--2018|Maddison Project (2018)]] ; population from 1950 to 1970: [[#UN--2015|UN (2015)]] ; population and GDP for 1960–2020: [[#World%20Bank--2021|World Bank (2021)]] ; data on material stock, extraction, and use of secondary materials: [[#Wiedenhofer--2019|Wiedenhofer et al. (2019)]] ; data on material extraction: UNEP and IRP (2020); industrial energy use for 1900–1970: [[#IIASA--2018|IIASA (2018)]] , for 1971–2019: [[#IEA--2021b|IEA (2021b)]] ; data on industrial GHG emissions for 1900–1970: [[#CDIAC--2017|CDIAC (2017)]] , for 1970–2019: data from [[#Crippa--2021|Crippa et al. (2021)]] and [[#Minx--2021|Minx et al. (2021)]] .

There are two major concepts of '''material efficiency''' ( ''ME'' ). The broader one highlights demand reduction via policies promoting more intensive use, assuming sufficient (excluding luxury) living space or car ownership providing appropriate service levels – housing days or miles driven and life-time extension ( [[#Hertwich--2019|Hertwich et al. 2019]] , 2020). This approach focuses on dematerialisation of society ( [[#Lechtenböhmer--2020|Lechtenböhmer and Fischedick 2020]] ), where a ‘dematerialisation multiplier’ ( [[#Pauliuk--2021|Pauliuk et al. 2021]] ) limits both material stock and GDP growth, as progressively fewer materials are required to build and operate the physical in-use stock to deliver sufficient services. According to the IRP (2020), reducing floor space demand by 20% via shared and smaller housing compared to the reference scenario would decrease Group of Seven (G7) countries’ GHG emissions from the material-cycle of residential construction up to 70% in 2050. The narrower concept ignores demand and sufficiency aspects and focuses on supply chains considering ''ME'' as less basic materials use to produce a certain final product, for example, a car or a metre squared of living space ( [[#OECD--2019a|OECD 2019a]] ; [[#IEA--2020a|IEA 2020a]] ). No matter if the broader or the narrower concept of ''ME'' is applied, in 1970–2019 it did not contribute much to the decoupling of industrial emissions from GDP. This is expected to change in the future ( ).

Material efficiency analysis mostly uses material intensity or productivity indicators, which compare material extraction or consumption with GDP ( [[#Oberle--2019|Oberle et al. 2019]] ; [[#Hertwich--2020|Hertwich et al. 2020]] ). Those indicators are functions of '''material stock intensity of GDP''' (tonnes per dollar) and material intensity of building and operating accumulated in-use stock. Coupling services or GDP with the built stock allows for a better evaluation of demand for primary basic materials ( [[#Müller--2011|Müller et al. 2011]] ; [[#Liu--2013|Liu et al. 2013]] ; Liu and Müller 2013; [[#Pauliuk--2013a|Pauliuk et al. 2013a]] ; [[#Cao--2017|Cao et al. 2017]] ; [[#Wiedenhofer--2019|Wiedenhofer et al. 2019]] ; [[#Hertwich--2020|Hertwich et al. 2020]] ; [[#Krausmann--2020|Krausmann et al. 2020]] ). Since 1970 material stock growth driven by industrialisation and urbanisation slightly exceeded that of GDP and there was no decoupling, [[#footnote-024|3]] so in Kaya-like identities material stock may effectively replace GDP. There are different methods to estimate the former (see reviews in Pauliuk et al. (2015, 2019) and [[#Wiedenhofer--2019|Wiedenhofer et al. (2019)]] , the results of which are presented for major basic materials with some geographical resolution (Liu and Müller 2013; [[#Pauliuk--2013a|Pauliuk et al. 2013a]] ) or globally ( [[#Graedel--2011|Graedel et al. 2011]] ; [[#Geyer--2017|Geyer et al. 2017]] ; [[#Krausmann--2018|Krausmann et al. 2018]] ; [[#Pauliuk--2019|Pauliuk et al. 2019]] ; [[#Wiedenhofer--2019|Wiedenhofer et al. 2019]] ; [[#International%20Aluminium%20Institute--2021a|International Aluminium Institute 2021a]] ).

For a subset of materials, such as solid wood, paper, plastics, iron/steel, aluminium, copper, other metals/minerals, concrete, asphalt, bricks, aggregate, and glass, total in-use stock escalated from 36 Gt back in 1900 to 186 Gt in 1970, 572 Gt in 2000, and 960 Gt in 2015, and by 2020 it exceeded 1,100 Gt, or 145 tonnes per capita ( [[#Krausmann--2018|Krausmann et al. 2018]] , 2020; [[#Wiedenhofer--2019|Wiedenhofer et al. 2019]] ). In 1900–2019, the stock grew 31-fold, which is strongly coupled with GDP growth (36-fold). As the UK experience shows, material stock intensity of GDP may ultimately decline after services fully dominate GDP, and this allows for material productivity improvements to achieve absolute reduction in material use, as stock expansion slows down ( [[#Streeck--2020|Streeck et al. 2020]] ). While the composition of basic materials within the stock of manufactured capital was evolving significantly, overall stock use associated with a unit of GDP has been evolving over the last half-century in a quite narrow range of 7.7–8.6 t per USD1000 (2017 purchasing power parity (PPP)) showing neither signs of decoupling from GDP, nor saturation as of yet. Mineral building materials (concrete, asphalt, bricks, aggregate, and glass) dominate the stock volume by mass (94.6% of the whole stock, with the share of concrete alone standing at 43.5%), followed by metals (3.5%) and solid wood (1.4%). The largest part of in-use stock of our ‘cementing societies’ ( [[#Cao--2017|Cao et al. 2017]] ) is constituted by concrete: about 417 Gt in 2015; [[#Krausmann--2018|Krausmann et al. (2018)]] extrapolated this to 478 Gt (65 tonnes per capita) in 2018, which contains about 88 Gt of cement. [[#footnote-023|4]] The iron and steel stock is assessed at 25–35 Gt ( [[#Wiedenhofer--2019|Wiedenhofer et al. 2019]] ; [[#Gielen--2020|Gielen et al. 2020]] ; [[#Wang--2021|Wang et al. 2021]] ), while the plastics stock reached 2.5–3.2 Gt ( [[#Geyer--2017|Geyer et al. 2017]] ; [[#Wiedenhofer--2019|Wiedenhofer et al. 2019]] ; [[#Saygin--2021|Saygin and Gielen 2021]] ) and the aluminium stock approached 1.1 Gt ( [[#International%20Aluminium%20Institute--2021a|International Aluminium Institute 2021a]] ), or just 0.1% of the total. In sharp contrast to global energy intensity, which has more than halved since 1900 ( [[#Bashmakov--2019|Bashmakov 2019]] ), in 2019 material stock intensity (in-use stock of manufactured capital per GDP) was only 14% below the 1900 level, but 15% above the 1970 level. In-use stock per capita has been growing faster than GDP per capita since 2000 (Figure 11.3). The growth rate of total in-use stock of manufactured capital was 3.8% in 1971–2000 and 3.5% in 2000–2019, or 32–35 Gt yr –1 , to which concrete and aggregates contributed 88%. Recent demand for stockbuilding materials was 51–54 Gt yr –1 , to which recycled materials recently contributed only about 10% of material input. About 46–49 Gt yr –1 was virgin inputs, which after accounting for processing waste and short-lived products (over 8 Gt yr –1 ) scale up to 54–58 Gt yr –1 of primary extraction ( [[#Krausmann--2017|Krausmann et al. 2017]] , 2018; UNEP and IRP 2020). The above indicates that we have only begun to exploit the potential for recycling and circularity more broadly.

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'''Figure 11.3 | Raw natural materials extraction since 1970.''' In windows: left – growth of population, GDP and basic materials production (1990 = 100) in 1990–2020; right – in-use stock per capita vs income level (1900–2018; brown dots are for 2000–2018). The regressions provided show that for more recent years elasticity of material stock to GDP was greater than unity, comparing with the lower unity in preceding years. Source: developed based on [[#Maddison%20Project--2018|Maddison Project (2018)]] ; [[#Wiedenhofer--2019|Wiedenhofer et al. (2019)]] ; [[#IEA--2020b|IEA (2020b)]] ; UNEP and IRP (2020); [[#International%20Aluminium%20Institute--2021a|International Aluminium Institute (2021a)]] ; Statista (2021a,b); U.S. Geological Survey (2021); [[#World%20Bank--2021|World Bank (2021)]] ; [[#World%20Steel%20Association--2021|World Steel Association (2021)]] .

Total '''extraction of all basic materials''' (including biomass and fuels) in 2017 reached 92 Gt yr –1 , which is 13 times above the 1900 level (Figure 11.3). [[#footnote-022|5]] When recycled resources are added, total material inputs exceed 100 Gt ( [[#Circle%20Economy--2020|Circle Economy 2020]] ). In Equation 11.1 ''MPR'' represents only material inputs to the stock, excluding dissipative use – biomass (food and feed) and combusted fuels. Total extraction of stock building materials (metal ores and non-metallic minerals) in 2017 reached 55 Gt yr –1 . [[#footnote-021|6]] In 1970–2018, it grew 4.3-fold and the ratio of ''MPR'' to accumulated in-use capital has nearly been constant since 1990 along with ratio to GDP (Figure 11.3).

End-of-life waste from accumulated stocks along with (re)-manufacturing and construction waste is assessed at 16 Gt yr –1 in 2014 and can be extrapolated in 2018 to 19 Gt yr –1 ( [[#Krausmann--2018|Krausmann et al. 2018]] ; [[#Wiedenhofer--2019|Wiedenhofer et al. 2019]] ), or 1.8% from stock of manufactured capital. Less than 6 Gt yr –1 was recycled and used to build the stock (about 10% of inputs). [[#footnote-020|7]] While the circularity gap is still large, and limited circularity was engineered into accumulated stocks, [[#footnote-019|8]] '''material recycling''' mitigated some GHG emissions by replacing energy-intensive virgin materials. [[#footnote-018|9]] When the stock saturates, in closed material loops the end-of-life materials waste has to be equal to material input, and primary production therefore has to be equal to end-of-life waste multiplied by unity minus recycling rate. When the latter grows, as the linear metabolism is replaced with the circular one, the share of primary materials production in total material input declines.

Recycling rates for metals are higher than for other materials: the end-of-life scrap input ratio for 13 metals is over 50%, and stays in the range of 25–50% for another ten, but even for metals recycling flows fail to match the required inputs ( [[#Graedel--2011|Graedel et al. 2011]] ). Globally, despite overall recycling rates being at 85%, the all-scrap ratio for steel production in recent years stays close to 35–38% ( [[#Gielen--2020|Gielen et al. 2020]] ; [[#IEA--2021b|IEA 2021b]] ) ranging from 22% in China (only 10% in 2015) to 69% in the US and to 83% in Turkey ( [[#BIR--2020|BIR 2020]] ). For end-of-life scrap this ratio declined from 30% in 1995–2010 to 21–25% after 2010 ( [[#Gielen--2020|Gielen et al. 2020]] ; [[#Wang--2021|Wang et al. 2021]] ).

For aluminium, the share of scrap-based production grew from 17% in 1962 to 34% in 2010 and stabilised at this level until 2019, while the share of end-of-life scrap grew from 1.5% in 1962 to nearly 20% in 2019 ( [[#International%20Aluminium%20Institute--2021a|International Aluminium Institute 2021a]] ). The global recycling (mostly mechanical) rate for plastics is only 9–10% [[#footnote-017|10]] ( [[#Geyer--2017|Geyer et al. 2017]] ; [[#Saygin--2021|Saygin and Gielen 2021]] ), and that for paper progressed from 34% in 1990 to 44% in 2000 and to over 50% in 2014–2018 ( [[#IEA--2020b|IEA 2020b]] ).

The limited impacts of material efficiency factors on industrial GHG emissions trends reflect the lack of integration of material efficiency in energy and climate policies which partly results from the inadequacy of monitored indicators to inform policy debates and set targets; [[#footnote-016|11]] lack of high-level political focus and industrial lobbying; uncoordinated policy across institutions and sequential nature of decision-making along supply chains; carbon pricing policy lock-in with upstream sectors failing to pass carbon costs on to downstream sectors (due to compensation mechanisms to reduce carbon leakage) and so have no incentives to exploit such options as light-weighting, reusing, remanufacturing, recycling, diverting scrap, extending product lives, using products more intensely, improving process yields, and substituting materials ( [[#Skelton--2017|Skelton and Allwood 2017]] ; [[#Gonzalez%20Hernandez--2018b|Gonzalez Hernandez et al. 2018b]] ; [[#Hilton--2018|Hilton et al. 2018]] ). Poor progress with material efficiency is part of the reason why industrial GHG emissions are perceived as ‘hard to abate’, and many industrial low-carbon trajectories to 2050 leave up to 40% of emissions in place ( [[#Material%20Economics--2019|Material Economics 2019]] ; [[#IEA--2021a|IEA 2021a]] ). The importance of this factor activation rises as in-use material stock is expected to scale up by a factor of 2.2–2.7 to reach 2215–2720 Gt by 2050 ( [[#Krausmann--2020|Krausmann et al. 2020]] ). Material extraction in turn is expected to rise to 140–200 Gt yr –1 by 2060 ( [[#OECD--2019a|OECD 2019a]] ; [[#Hertwich--2020|Hertwich et al. 2020]] ) providing unsustainable pressure on climate and environment and calling for fundamental improvements in material productivity.

In 2014–2019, the average annual growth rate (AAGR) of global '''industrial energy use''' was 0.4% compared to 3.2% in 2000–2014, following new policies and trends, particularly demonstrated by China [[#footnote-015|12]] ( [[#IEA--2020b|IEA 2020b]] ,d). Whatever metric is applied, industry (coal transformation, mining, quarrying, manufacturing and construction) driven mostly by material production, dominates global energy consumption. About two fifths of energy produced globally goes to industry, directly or indirectly. Direct energy use (including energy used in coal transformation) accounts for nearly 30% of total final energy consumption. When supplemented by non-energy use, the share for the post-AR5 period (2015–2019) stands on average close to 40% of final energy consumption, and at 28.5% of primary energy use. [[#footnote-014|13]] With an account of indirect energy use for the generation of power and centralised heat to be consumed in industry, the latter scales up to 37%. Industrial energy use may be split by: material production and extraction (including coal transformation): 51% on average for 2015–2019; non-energy use (mostly chemical feedstock): 22% [[#footnote-013|14]] ; and other energy use (equipment, machinery, food and tobacco, textiles, leather, etc.): 27%. Energy use for material production and feedstock [[#footnote-012|15]] makes about three quarters (73%) of industrial energy consumption and is responsible for 77% of its increment in 2015–2019 (based on [[#IEA--2021a|IEA 2021a]] ).

For over a century, '''industrial energy efficiency''' improvements have partially offset growth in GHG emissions. Industrial energy use per tonne of extracted materials (ores and building materials as a proxy for materials going through the whole production chain to final products) fell by 20% in 2000–2019 and by 15% in 2010–2019, accelerated driven by high energy prices to 2.4% yr –1 in 2014–2019, matching the values observed back in 1990–2000 (Figure 11.2). Assessed per value added using market exchange rates, industrial energy intensity globally dropped by 12% in 2010–2018, after its 4% decline in 2000–2010, resulting in 2000–2018 decline by 15% ( [[#IEA--2020b|IEA 2020b]] ,a). The 2020 COVID crisis slowed down energy intensity improvements by shifting industrial output towards more energy-intensive basic materials ( [[#IEA--2020e|IEA 2020e]] ). Specific energy consumption per tonne of iron and steel, chemicals and cement production in 2019 was about 20% below the 2000 level ( [[#IEA--2020b|IEA 2020b]] ,a). This progress is driven by moving towards best available technologies (BATs) for each product through new and highly efficient production facilities in China, India and elsewhere, and by the contribution from recycled scrap metals, paper and cardboard.

Physical energy intensity for the production of materials typically declines and then stabilises at the BAT level once the market is saturated, unless a transformative new technology enters the market ( [[#Gutowski--2013|Gutowski et al. 2013]] ; [[#Crijns-Graus--2020|Crijns-Graus et al. 2020]] ; [[#IEA--2021a|IEA 2021a]] ). Thus, the energy saving effect of switching to secondary used material comes to the forefront, as energy consumption per tonne for many basic primary materials approach the BATs. This highlights the need to push towards circular economy, materials efficiency, reduced demand, and fundamental process changes (e.g., towards electricity and hydrogen-based steel making). Improved recycling rates allow for a substantial reduction in energy use along the whole production chain – material extraction, production, and assembling – which is in great excess of energy used for collection, separation, treatment, and scrap recycling minus energy used for scrap landfilling. The International Energy Agency ( [[#IEA--2019b|IEA 2019b]] ) estimates that by increasing the recycling content of fabricated metals, average specific energy consumption (SEC) for steel and aluminium may be halved by 2060. Focusing on whole systems ‘integrative design’ expands efficiency resource much beyond the sum of potentials for individual technologies. Material efficiency coupled with energy efficiency can deliver much greater savings than energy efficiency alone. [[#Gonzalez%20Hernandez--2018b|Gonzalez Hernandez et al. (2018b)]] stress that presently about half of steel or aluminium are scrapped in production or oversized for targeted services. They show that resource efficiency expressed in exergy as a single metric for both material and energy efficiency for the global iron and steel sector is only 33%, while secondary steel-making is about twice as efficient (66%) as ore-based production (29%). While shifting globally in ore-based production from the average to the best available level can save 6.4 EJ yr –1 , the saving potential of shifting to secondary steel-making is 8 EJ yr –1 , and is limited mostly by scrap availability and steel quality requirements.

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