Using a Linear Regression Approach to Sequential Interindustry Model for Time-Lagged Economic Impact Analysis

The input-output (IO) model is a powerful economic tool with many extended applications. However, one of the widely criticized drawbacks is its rather lengthy time lag in data preparation, making it impossible to apply IO in high-resolution time-series analysis. The conventional IO model is thus unfortunately unsuited for time-series analysis. In this study, we present an innovative algorithm that integrates linear regression techniques into a derivative of the IO method, the Sequential Interindustry Model (SIM), to overcome the inherent shortcomings of statistical lags in conventional IO studies. The regressed relationship can thus be used to predict, in the short term, the accumulated chronological impacts induced by fluctuations in sectorial economic demands under disequilibrium conditions. A simulated calculation is presented to serve as an illustration and verification of the new method. In the future, this application can be extended beyond economic studies to broader problems of system analysis

The Polarizing Trend of Regional CO2 Emissions in China and Its Implications

CO2 emissions are unevenly distributed both globally and regionally within nation-states. Given China's entrance into the new stage of economic development, an updated study on the largest CO2 emitter's domestic emission distribution is needed for effective and coordinated global CO2 mitigation planning. We discovered that domestic CO2 emissions in China are increasingly polarized for the 2007-2017 period. Specifically, the domestically exported CO2 emissions from the less developed and more polluting northwest region to the rest of China has drastically increased from 165 Mt in 2007 to 230 Mt in 2017. We attribute the polarizing trend to the simultaneous industrial upgrading of all regions and the persistent disparity in the development and emission decoupling of China's regions. We also noted that CO2 emissions exported from China to the rest of the world has decreased by 41% from 2007 to 2017, with other developing countries filling up the vacancy. As this trend is set to intensify, we intend to send an alarm message to policy makers to devise and initiate actions and avoid the continuation of pollution migration

Using crowdsourced data to estimate the carbon footprints of global cities

Cities are at the forefront of the battle against climate change. However, intercity comparisons and responsibility allocations among cities are hindered because cost- and time-effective methods to calculate the carbon footprints of global cities have yet to be developed. Here, we establish a hybrid method integrating top-down input–output analysis and bottom-up crowdsourced data to estimate the carbon footprints of global cities. Using city purchasing power as the main predictor of the carbon footprint, we estimate the carbon footprints of 465 global cities in 2020. Those cities comprise 10% of the global population but account for 18% of the global carbon emissions showing a significant concentration of carbon emissions. The Gini coefficients are applied to show that global carbon inequality is less than income inequality. In addition, the increased carbon emissions that come from high consumption lifestyles offset the carbon reduction by efficiency gains that could result from compact city design and large city scale. Large climate benefits could be obtained by achieving a low-carbon transition in a small number of global cities, emphasizing the need for leadership from globally important urban centres

Green Strategies in Mobility Planning Towards Climate Change Adaption of Urban Areas Using Fuzzy 2D Algorithm

Urban mobility planning must urgently confront the challenges attendant to the low carbon transition and green transformation. The necessary paradigm shift from the traditional approaches to embracing environmental sustainability requires maintaining a firm and stable balancing act between opposing forces. The policy-making process in the transition period is complex and requires a detailed analysis that the academic literature lacks. This study analyzes the decision-making process for urban mobility planning to contribute the academic literature on sustainable transitions. In order to illustrate the complexities in the decision-making process, we design an original case scenario. In the case, the planners are supposed to choose the best project from among four recent green strategies. In the process, they need to take the conflicting requirements on the social, economic, environmental and technical issues into account. Sixteen constraints reflect the available physical and financial conditions. Because the decision-making process includes complexities, a novel two-stages model is introduced in the method that is used to solve the problem. In the first stage, the fuzzy D PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) algorithm is applied to determine the weights. In the second stage, the fuzzy D Dombi (fuzzy 2D) algorithm is proposed to evaluate the alternatives. The results show that societal dynamics are crucially important in choosing the best alternative. Among four alternatives, the one that is inclusive and makes the existing investments more efficient is highly prioritized. Our findings offer policy implications emphasizing the importance of green mobility projects that favors the social benefits as well as financial issues

Using crowdsourced data to estimate the carbon footprints of global cities

Cities are at the forefront of the battle against climate change. However, intercity comparisons and responsibility allocations among cities are hindered because cost- and time-effective methods to calculate the carbon footprints of global cities have yet to be developed. Here, we establish a hybrid method integrating top-down input–output analysis and bottom-up crowdsourced data to estimate the carbon footprints of global cities. Using city purchasing power as the main predictor of the carbon footprint, we estimate the carbon footprints of 465 global cities in 2020. Those cities comprise 10% of the global population but account for 18% of the global carbon emissions showing a significant concentration of carbon emissions. The Gini coefficients are applied to show that global carbon inequality is less than income inequality. In addition, the increased carbon emissions that come from high consumption lifestyles offset the carbon reduction by efficiency gains that could result from compact city design and large city scale. Large climate benefits could be obtained by achieving a low-carbon transition in a small number of global cities, emphasizing the need for leadership from globally important urban centres

The Slowdown in China's Carbon Emissions Growth in the New Phase of Economic Development

China's CO2 emissions have plateaued under its commitment to reaching peak carbon emissions before 2030 in order to mitigate global climate change. This commitment is aligned with China's turn toward more sustainable development, named “the new normal” phase. This study aims to explore the role of possible socioeconomic drivers of China's CO2 emission changes by using structural decomposition analysis (SDA) for 2002–2017. The results show deceleration of China's annual emissions growth from 10% (2002–2012) to 0.3% (2012–2017), which is mainly caused by gains in energy efficiency, deceleration of economic growth, and changes in consumption patterns. Gains in energy efficiency are the most important determinants, offsetting the increase by 49% during 2012–2017. The recent moderation of emission growth is also attributed to China's decelerating annual growth rate of gross domestic product (GDP) per capita from 12% (2002–2012) to 6% (2012–2017) and to the economic transformation to consumption-led patterns in the new normal phase

China's energy consumption and economic activity at the regional level

Since 2013, China's economy has undergone a series of major structural changes under the new normal. This study aimed to research China's plateauing regional-level energy consumption at this stage by analysing socioeconomic factors driving energy consumption changes from 2002 to 2019 through decomposition analysis and regional value chains. The results indicate that the annual growth rate of China's energy consumption dropped from 10% between 2002 and 2013 to 2% between 2013 and 2019, mainly attributable to energy efficiency enhancement offsetting the −27% increase from 2013 to 2019 and structural changes. At the regional level, the three structural drivers were closely related, including the regional structure, industrial structure and energy structure. Under the new normal, the −2.58% contribution of the regional structure to energy consumption growth was mainly made by regions with a high energy efficiency; one way to improve the energy efficiency was to upgrade the regional industrial structure, leading to the slowdown by 0.26%; and industrial transition could be accompanied by adjustment of the energy structure towards relatively clean energy, thereby offsetting growth by −0.13%. The energy consumption required to create value-added outflows along regional value chains varied greatly across regions, sectors and years

Regional development and carbon emissions in China

China announced at the Paris Climate Change Conference in 2015 that the country would reach peak carbon emissions around 2030. Since then, widespread attention has been devoted to determining when and how this goal will be achieved. This study aims to explore the role of China’s changing regional development patterns in the achievement of this goal. This study uses the logarithmic mean Divisia index (LMDI) to estimate seven socioeconomic drivers of the changes in CO2 emissions in China since 2000. The results show that China’s carbon emissions have plateaued since 2012 mainly because of energy efficiency gains and structural upgrading (i.e., industrial structure, energy mix and regional structure). Regional structure, measured by provincial economic growth shares, has drastically reduced CO2 emissions since 2012. The effects of these drivers on emissions changes varied across regions due to their different regional development patterns. Industrial structure and energy mix resulted in emissions growth in some regions, but these two drivers led to emissions reduction at the national level. For example, industrial structure reduced China’s CO2 emissions by 1.0% from 2013-2016; however, it increased CO2 emissions in the Northeast and Northwest regions by 1.7% and 0.9%, respectively. By studying China’s plateauing CO2 emissions in the new normal stage at the regional level, it is recommended that regions cooperate to improve development patterns