[et_pb_section bb_built=”1″ admin_label=”Section” fullwidth=”on” specialty=”off”][et_pb_fullwidth_post_title admin_label=”Sustainable transport through agent-based modeling” title=”on” meta=”off” author=”on” date=”on” categories=”on” comments=”on” featured_image=”on” featured_placement=”background” parallax_effect=”off” parallax_method=”on” text_orientation=”center” text_color=”dark” text_background=”on” text_bg_color=”rgba(255,255,255,0.73)” use_border_color=”off” border_color=”#ffffff” border_style=”solid” custom_css_main_element=”padding-bottom: 10px;” custom_padding=”15%||3%|” \/][\/et_pb_section][et_pb_section bb_built=”1″ admin_label=”Section” fullwidth=”off” specialty=”off” transparent_background=”off” background_color=”rgba(12,113,195,0.16)” allow_player_pause=”off” inner_shadow=”off” parallax=”off” parallax_method=”off” make_fullwidth=”off” use_custom_width=”off” width_unit=”on” make_equal=”off” use_custom_gutter=”off” custom_padding=”0px|0px|0px|0px”][et_pb_row admin_label=”Row”][et_pb_column type=”4_4″][et_pb_text admin_label=”TEASER” background_layout=”light” text_orientation=”left” use_border_color=”off” border_color=”#ffffff” border_style=”solid”]<\/p>\n
People\u2019s environmental actions<\/strong>\u2014<\/strong>such as buying an electric car<\/strong>\u2014<\/strong>are profoundly influenced by those around them, with social network interactions and peer effects compensating for a decade\u2019s delay in carbon tax introduction and allowing it to be 30% lower. These findings were the results of pioneering agent-based modeling work from the IIASA Transitions to New Technologies Program (TNT).<\/strong><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section bb_built=”1″ admin_label=”section” transparent_background=”off” allow_player_pause=”off” inner_shadow=”off” parallax=”off” parallax_method=”off” custom_padding=”0px|0px|0px|0px” make_fullwidth=”off” use_custom_width=”off” width_unit=”on” make_equal=”off” use_custom_gutter=”off”][et_pb_row admin_label=”Row”][et_pb_column type=”4_4″][et_pb_text admin_label=”BODY” background_layout=”light” text_orientation=”left” use_border_color=”off” border_color=”#ffffff” border_style=”solid”]<\/p>\n Individual choices and environmental awareness are an essential part of achieving sustainability; not least because people are more likely to make sustainable choices if those around them do. These so-called peer effects were the focus of work as part of the Alternative Pathways to Sustainable Development and Climate Stabilization<\/em> project, a joint effort between the TNT and Energy<\/a> (ENE) programs, and colleagues at the Research Institute for Innovative Technologies for the Earth, Japan. After much model development in previous years, the novel agent-based models were sufficiently developed in 2016 to be tested in empirical calibrations with a focus on vehicle choice adoption and transportation systems transitions, which are a traditional weakness of highly aggregated integrated assessment models.<\/p>\n As a calibration exercise, TNT researchers used an agent-based model to replicate the results of a discrete-choice model of the vehicle market in North America, which had been developed by ENE and colleagues.\u00a0The excellent congruence between these two contrasting modeling approaches enabled the researchers to isolate the effects of social network interactions and peer effects in agent-based modeling scenarios. The model simulations meant they could quantify the market impact of social network and peer effects: by \u201cswitching them off,\u201d they could determine how much earlier and higher traditional economic incentives such as carbon taxes would have to be to yield comparable market outcomes.<\/p>\n The results are highly instructive for climate policy. Social network and peer effects\u2014which can be enhanced by new information and communication technologies\u2014can compensate for a decade\u2019s delay in carbon tax introduction and allow the tax to be 30% lower. Motivating environmentally conscious consumers can therefore be an effective climate policy, especially in cases where early and sufficiently stringent economic climate policy is not possible.<\/p>\n In a second calibration exercise, TNT researchers modified the existing agent-based model of vehicle choice and tested it using real-world data on the vehicle market (conventional and electric) in Shanghai, China. The city was chosen because the electric vehicle market is particularly large and dynamic (rivalling markets such as California, USA, or Norway).<\/p>\n The researchers used historical data for both conventional and electric vehicles and then performed simulations of future market growth under a range of policy scenarios. Currently, economic incentives for electric vehicle purchases in Shanghai are unparalleled, including both federal and local government subsidies, as well as a waiver on a car registration fee that is roughly the price of a medium-sized car.<\/p>\n If these strong economic incentives continue, a market penetration of electric vehicles of 80% by 2040 is possible in the scenarios modeled. Social network and peer effects can compensate, to a degree, for a possible weakening of the substantial (and costly) economic incentives to adopt zero-emission vehicles. Alternatively, continued strong policy incentives could yield a complete transformation to zero-emissions urban mobility based on non-motorized mobility (i.e., walking and cycling), electrified public transport, and electric vehicles.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section bb_built=”1″ admin_label=”Section” fullwidth=”off” specialty=”off” transparent_background=”off” background_color=”rgba(12,113,195,0.16)” allow_player_pause=”off” inner_shadow=”off” parallax=”off” parallax_method=”off” make_fullwidth=”off” use_custom_width=”off” width_unit=”on” make_equal=”off” use_custom_gutter=”off” custom_padding=”0px|0px|0px|0px”][et_pb_row admin_label=”Row” make_fullwidth=”off” use_custom_width=”off” width_unit=”on” use_custom_gutter=”off” allow_player_pause=”off” parallax=”off” parallax_method=”off” make_equal=”off” parallax_1=”off” parallax_method_1=”off” custom_width_px=”1220px” parallax_2=”off” parallax_method_2=”off”][et_pb_column type=”1_2″][et_pb_text admin_label=”References” background_layout=”light” text_orientation=”left” use_border_color=”off” border_color=”#ffffff” border_style=”solid”]<\/p>\n [1] Zhang Y, Chen H, & Ma T (2016). System optimization model of adoption of a new infrastructure with multi-resource and multi-demand sites<\/a>. Journal of Systems Science and Systems Engineering <\/em>25 (1): 62-76. [\/et_pb_text][et_pb_text admin_label=”Collaborators” background_layout=”light” text_orientation=”left” use_border_color=”off” border_color=”#ffffff” border_style=”solid”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" More >><\/p>\n","protected":false},"author":2,"featured_media":3641,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[11,14,22,1,58],"tags":[],"class_list":["post-3601","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-americas","category-asia","category-other","category-leadership","category-transitions-to-new-technologies","wpautop"],"jetpack_sharing_enabled":false,"jetpack_featured_media_url":"https:\/\/ar16.iiasa.ac.at\/wp-content\/uploads\/sites\/2\/2017\/02\/TNT_Newroutes2.jpg","jetpack_shortlink":"https:\/\/wp.me\/p8n9wa-W5","_links":{"self":[{"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/posts\/3601","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/comments?post=3601"}],"version-history":[{"count":0,"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/posts\/3601\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/media\/3641"}],"wp:attachment":[{"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/media?parent=3601"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/categories?post=3601"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ar16.iiasa.ac.at\/wp-json\/wp\/v2\/tags?post=3601"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}References<\/h3>\n
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[2] Zhao J & Ma T (2016). Optimizing layouts of initial AFV refueling stations targeting different drivers, and experiments with agent-based simulations<\/a>. European Journal of Operational Research <\/em>249 (2): 706-716.
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[3] Zhao J & Ma T (2016). Optimizing layouts of initial refueling stations for alternative-fuel vehicles and experiments with agent-based simulations<\/a>. Simulation<\/em> 92 (3): 251-266.
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[4] Zhao J & Ma T (2016). Optimizing the initial setting of complex adaptive systems-optimizing the layout of initial AFVs stations for maximizing the diffusion of AFVs<\/a>. Complexity<\/em> 21 (1): 275-290. [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″][et_pb_button admin_label=”Button” button_url=”http:\/\/ar16.iiasa.ac.at\/tnt\/” url_new_window=”off” button_text=”Transitions to New Technologies Program” button_alignment=”left” background_layout=”dark” custom_button=”on” button_text_size=”15″ button_bg_color=”#006ac1″ button_border_width=”0″ button_border_radius=”20″ button_letter_spacing=”0″ button_use_icon=”on” button_icon=”%%67%%” button_icon_color=”#ffffff” button_icon_placement=”left” button_on_hover=”on” button_border_color_hover=”#5590c1″ button_border_radius_hover=”20″ button_letter_spacing_hover=”0″ \/][et_pb_text admin_label=”Contributors” background_layout=”light” text_orientation=”left” use_border_color=”off” border_color=”#ffffff” border_style=”solid”]<\/p>\nIIASA contributors<\/h3>\n
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Collaborators<\/h3>\n
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