NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 1 D espite two decades of effort to curb emissions of CO 2 and other greenhouse gases (GHGs), emissions grew faster during the 2000s than in the 1990s 1 , and by 2010 had reached ~50 Gt CO 2 equivalent (CO 2 eq) yr −1 (refs 2,3). The continuing rise in emissions is a growing challenge for meeting the international goal of limiting warming to less than 2 °C relative to the pre-industrial era, particularly without stringent climate policies to decrease emissions in the near future 2-4 . As negative emissions technologies (NETs) seem ever more necessary 3,[5][6][7][8][9][10] To have a >50% chance of limiting warming below 2 °C, most recent scenarios from integrated assessment models (IAMs) require large-scale deployment of negative emissions technologies (NETs). These are technologies that result in the net removal of greenhouse gases from the atmosphere. We quantify potential global impacts of the different NETs on various factors (such as land, greenhouse gas emissions, water, albedo, nutrients and energy) to determine the biophysical limits to, and economic costs of, their widespread application. Resource implications vary between technologies and need to be satisfactorily addressed if NETs are to have a significant role in achieving climate goals.options, to be able to decide which pathways are most desirable for dealing with climate change.There are distinct classes of NETs, such as: (1) bioenergy with carbon capture and storage (BECCS) 11,12 ; (2) direct air capture of CO 2 from ambient air by engineered chemical reactions (DAC) 13,14 ; (3) enhanced weathering of minerals (EW) 15 , where natural weathering to remove CO 2 from the atmosphere is accelerated and the products stored in soils, or buried in land or deep ocean [16][17][18][19] ; (4) afforestation and reforestation (AR) to fix atmospheric carbon in biomass and soils [20][21][22] ; (5) manipulation of carbon uptake by the ocean, either

Our joint statement suggests three main avenues of effort for the scientific and technological community in supporting human needs, maintaining the environment, and moving toward sustainable human consumption patterns. I want to speak on the second of these-the need to actively generate new knowledge. To do so, we need to change science itself, to go beyond what we already know and expand the world's capacity system for discovering new things.

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Scenarios limiting global warming to 1.5°C describe major transformations in energy supply and everrising energy demand. Here we provide a contrasting perspective by developing a narrative of future change based on observable trends that results in low energy demand. We describe and quantify changes in activity levels and energy intensity in the Global North and South for all major energy services. We project that global final energy demand by 2050 reduces to 245 EJ, around 40% lower than today despite rising population, income and activity. Using an integrated assessment modelling framework, we show how changes in the quantity and type of energy services drive structural change in intermediate and upstream supply sectors (energy and land use). Down-sizing the global energy system dramatically improves the feasibility of low-carbon supply-side transformation. Our scenario meets the 1.5°C climate target as well as many Sustainable Development Goals, without relying on negative emission technologies. * Contingency reserve of 8 EJ is allocated equally to Global North and South respectively. Bunker fuels are reported at the global level only, consistent with current energy balances and emission accounting frameworks. Activity level units vary per end-use service and upstream sector: a billion m 2 of floor space; b trillion passengerkilometres; c billion tonnes of materials; d trillion tonne-kilometres.

Technological choices largely determine the long-term characteristics or industrial society, including impacts on the natural em·ironmcnt. However. the treatment o[ technology in existing models that are used to project economic and environmental [utures remains highly stylized. Based on work over two decades at llASA. we present a use[ul typology for technology analysis and discuss methods that can be used to analyze the impact or technological changes on the global environment, especially global warming. Our [ocus is energy technologies. the main sou rce or many atmospheric environmental problems. We show that much improved treatment o[technology is possible with a combination o[historical analysis and new modeling techniques. In the historical record , we identify characteristic "learning rates" that allow simple quantified characterization or the improvement in cost and performance due to cumulati,·e experience and investments. We also identi[y patterns. processes and timescales that typi[y the diffusion of new technologies in competitive markets. Technologies that are long-lived and are components of interlocking networks typically require the longest tim e to diffuse and co-evolve with other technologies in the network ; such network effects yield high barriers to entry even [o r superior competitors.These simple observations allow three improvements to modeling of technological change and its consequences for global environmental change. One is that the replacement of long-lived infrastructures over time has also replaced the fuels that power the economy to yield progressively mo re energy per unit of carbon pollution -from coal to oil to gas. Such replacement has "deca rbonizcd " the global primary energy supply 0.3'Yo per year. In contrast. most baseline projections for emissions of carbon, the chief cause of global warming. ignore this robust historical trend and show little or no decarbonization . A second improvement is that by incorporating learning cun·es and uncertainty into micro scale models it is possible to e11do!Je11011sly generate patterns of technological choice that mirror the real world. Those include S-shaped diffusion patterns and timescales of technological dynamics that arc consistent with histo rical experience: they also include endogenous generation o["surprises" such as the appearance of radically new technologies. Third. it is possible to include learning phenomena stylistically in macro-scale models; we show that doing so can yield projections with lessened cm·ironmcntal impacts without necessa rily incurring negative effect on the economy. Arriving on that path by the year c JOO depends on intenening actions. such as incentives to promote greater diversity in technology and lower barriers to entry for new infrastructures that could accelerate historical trends or dccarbonization. ['

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