Incentive modes and reducing emissions from deforestation and degradation: who can benefit most? Jichuan Sheng a, b, c, * , Jie Cao a, b , Xiao Han d , Zhuang Miao e a Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China b School of Economics and Management, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China c Institute for Urban and Environmental Studies, Chinese Academy of Social Science, 28 Shuguangxili, Beijing 10028, China d School of Geography, University of Melbourne, 221 Bouverie Street, Parkville, VIC 3010, Australia e School of Economics and Management, Taizhou University, 93 Jichuan Road South, Taizhou 225300, China article info Article history: Received 8 October 2015 Received in revised form 11 April 2016 Accepted 11 April 2016 Available online 22 April 2016 Keywords: REDDþ Deforestation Incentives Stakeholders Policy effectiveness abstract An incentive mechanism is key to succeed the investments in Reducing Emissions from Deforestation and Degradation programs. However, the diversification of determinants makes it difficult to establish one mode to best incentive stakeholders. This paper compares various incentive modes in benefiting stakeholders by simulating dynamic game models. It analyzes the profit-making of developers and landholders based on four incentive modes. The modes are preferential tax for developers, incentive of carbon offsets for developers, investment incentive for developers, and incentive of reducing emissions for landholders. This paper compares the effects of incentive modes on benefit distribution of stake- holders and contributes to a new dynamic game framework. The results show that: (i) the effects of incentives of carbon offsets for developers and incentives of reducing emissions for landholders are almost same for the stakeholders; (ii) a preferential tax can only make developers unilaterally benefit, and will not change landholder welfare; (iii) investment incentives for developers can make landholders' profits be increased, while the effects of incentives on developers' profits are uncertain. Finally, the numerical simulation is used to verify these hypotheses. The core implication is that for the design of REDDþ incentives, the government should combine various incentive modes to fulfill different objectives in policy making. © 2016 Elsevier Ltd. All rights reserved. 1. Introduction Carbon emissions caused by deforestation and degradation have accounted for 10e17 percent of the total carbon emissions caused by anthropogenic factors. It has been the main source of carbon emissions in many tropical countries with rainforest (Metz et al., 2007; van der Werf et al., 2009; Harris et al., 2012). Therefore, a mechanism named “Reducing Emissions from Deforestation and Degradation (REDDþ)” is introduced at the Bali Climate Change Conference to suggest incentives for developing countries to reduce emissions from the forest sector (UNFCCC, 2007). REDDþ is a global response to climate change mitigation working towards the lowest cost and highest efficiency (Stern, 2007). In the past, a variety of methods have been proposed to restrain the loss of forestry, yet failed to achieve expectations (Wunder, 2005; Forner et al., 2006; Skutsch and Trines, 2008). REDDþ, however, is an effective one that provides a new framework for emissions reduction from deforestation and degradation. The framework supports not only forest protection, but also sustainable forest management, biodi- versity conservation, and forest carbon storage (UNFCCC, 2009). In order to reduce atmospheric greenhouse gases (GHG), REDDþ employs appropriate incentives for local residents in developing countries to change deforestation-related behavior (Gregersen et al., 2010). Therefore, incentives as a core of REDDþ have attrac- ted attentions increasingly and deeply (Irawan et al., 2013; Duchelle et al., 2014; Loaiza et al., 2015). The incentive scheme is often regarded as an efficient policy tool to internalize forest carbon externality and promote REDDþ in developing countries (Angelsen, 2010; Pattanayak et al., 2010). * Corresponding author. Collaborative Innovation Center on Forecast and Evalu- ation of Meteorological Disasters, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China. E-mail address: shengjichuan@gmail.com (J. Sheng). Contents lists available at ScienceDirect Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro http://dx.doi.org/10.1016/j.jclepro.2016.04.042 0959-6526/© 2016 Elsevier Ltd. All rights reserved. Journal of Cleaner Production 129 (2016) 395e409