長野県松本市におけるフューチャー・デザインの研究と実践 : フューチャー・デザイン・ワークショップ マニュアル 基本編

本研究は，松本市役所職員と信州大学の研究者が組織する「地域政策研究会」の活動を対象に，「市民参加による政策形成過程」のデザイン手法であるFuture Design（以下，FD）の社会実装化の特徴と意義を整理し，FDワークショップ（以下，WS）の実施方法を明らかにするとともに，FDWSの効果を検証することを目的としている。　地域社会が抱える問題（人口流出，環境，防災等）は，現代世代とまだ生まれていない将来世代との間で，便益と負担に関する利害対立をはらむ。しかし，将来世代が政策立案・交渉の場に存在しないため，その解決は困難である。そこで提唱されたのがFDである。この手法の最大の特徴は，WS等を通じて参加市民に「将来世代」になり切ってもらい，将来世代にとって妥当な政策を議論することである。　本研究では，FDを実践するうえで重要となる，政策テーマの選定，WSのデザイン，ファシリテーション手法，WSの質的量的評価に関して，以下の結果が得られた。第1に，政策テーマの選定と事前準備である。松本市での実践では，テーマ選定とファシリテーションスキルの修得に関して，20回強の打ち合わせを行い，松本市・信州大学双方で理解を深める必要性が認められた。第2に，FDWSの構造化である。参加者は，同一テーマでとを経験し，成果を相対化する機会が必要となることが明らかとなった。第3に，ファシリテーションの重要性である。精緻でかつ明快なファシリテーションデザインにすることで，討議が迷走することを防ぐことが可能となった。第4に，WSの効果検証の必要性である。討議の内容をコーティングすることで，対立・因果・関連といった関係性をまとめることが可能となった。また「時間選好」を記録することで，FDによる介入効果を検証することができ，仮想将来世代への飛翔が認められた。第5に，FDWSの政策テーマ選定の難易度である。「世代を超えた公共性」があればすべてのテーマが討議可能であるが，政策決定の自由度と制作対象領域の広さの2つの視点から，FDとして比較的取り組みやすい政策テーマが明らかとなった。ArticleFaculty of Economics and Law Shinshu University Staff Paper Series. 2 : 1-30 (2020). (Staff Paper No.19-01).technical repor

Second-stage regression. Dependent variable: Per capita income.

<p>Parameter estimates for second-stage regressions that include biodiversity (columns a and b) and islands (column b) as IVs for disease. Standard errors are presented in parentheses next to their corresponding parameter estimates. Bold indicates significance at the 10% level. <i>n</i> = 139.</p>J<p>Based on Hansen's J statistic.</p>a<p>Units×10⁻².</p>**<p>p≤0.05.</p>***<p>p≤0.01.</p

Figure 5

<p>(Left) Partial correlation of biodiversity and the burden of VBPDs estimated from <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456.e005" target="_blank">equation (3)</a>. (Right) Relationship between per capita income and fitted value of VBPDs, , estimated from <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456.e005" target="_blank">equation (3)</a>.</p

Figure 1

<p>(Left) Per capita DALYs lost to VBPDs along a latitudinal gradient. (Right) Per capita income across latitude is inversely correlated with the burden of VBPDs <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456-Lopez1" target="_blank">[1]</a>–<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456-International1" target="_blank">[3]</a>.</p

Results. Two-step GMM estimates of simultaneous equations. Dependent variable: Income.

<p>From left to right, the number of control variables, which are listed on the left, increases in a stepwise fashion. The IVs for disease are variables that are in the disease equation (listed in the corresponding columns in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio-1001456-t005" target="_blank">Table 5</a>) but not in the income equation here. Robust standard errors are presented in parentheses next to their corresponding coefficient estimates. First-stage <i>F</i>-test indicates strength of IVs if there is only one endogenous variable (disease). If there are multiple endogenous variables (disease, institutions, and spatially lagged income), Shea's partial <i>R</i>² indicates strength of IVs <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456-Shea1" target="_blank">[75]</a>. Bold indicates significance at the 10% level. <i>n</i> = 139.</p>IV<p>Variable is instrumented.</p>J<p>Based on Hansen's J statistic.</p>a<p>Units×10⁻².</p>*<p>p≤0.10.</p>**<p>p≤0.05.</p>***<p>p≤0.01.</p

Results of simultaneous equations model.

<p>Columns 2 and 4 represent parameter estimates for the income and disease equations, which correspond to <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456.e001" target="_blank">equations (1</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456.e002" target="_blank">2)</a> in the text. The corresponding independent variables are listed in columns 1 and 3. The income, disease, and energy variables are natural logged. The estimated effect of disease on income is −0.40. This suggests that the average tropical country with a logged per capita burden of VBPDs of 1.99 would more than double their per capita income if their disease burden were reduced to that of an average temperate country of 0.19. The estimated effect of biodiversity on disease is −0.29. Thus, if the biodiversity index of a country like Indonesia (index = 663) were to lose 15% of its biodiversity (falling by 100), the burden of VBPDs would be expected to rise by about 30%. Robust standard errors are presented in parentheses below their corresponding coefficient estimates. First stage <i>F</i>-test is used in the second model (column 3) because there is only one endogenous variable (income). Because the first model (column 1) has multiple endogenous variables (disease and institutions), we use Shea's Partial <i>R</i>² as an indicator of the strength of correlation of the IVs <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456-Shea1" target="_blank">[75]</a>. Bold indicates significance at the 10% level (n = 139).</p>J<p>Based on Hansen's J statistic.</p>ln<p>Natural log.</p>a<p>Units×10⁻² units.</p>*<p>p≤0.10.</p>***<p>p≤0.01.</p

Schematic of the statistical model.

<p>The burden of VBPDs and income are estimated simultaneously, with exogenous geographic and ecological variables used as IVs. The IVs for disease are islands and species richness. These are strongly correlated with the disease burden but not independently correlated with income, and therefore can be used to make inference on the effect of disease on income.</p

First-stage regression. Dependent variable: Disease (VPBDs).

<p>Parameter estimates for first-stage regressions that include biodiversity (columns a and b) and islands (column b) as IVs. Standard errors are presented in parentheses next to their corresponding parameter estimates. Bold indicates significance at the 10% level. <i>n</i> = 139.</p>a<p>Units×10⁻².</p>***<p>p≤0.01.</p

Results. Two-step GMM estimates of simultaneous equations. Dependent variable: Disease.

<p>From left to right, the number of control variables increases in a stepwise fashion. The IVs for income are the variables that are in the income equation (listed in the corresponding columns in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio-1001456-t004" target="_blank">Table 4</a>) but not in the disease equation here. Robust standard errors are presented in parentheses next to their corresponding coefficient estimates. First-stage <i>F</i>-test indicates strength of IVs if there is only one endogenous variable (income). If there are multiple endogenous variables (income and spatially lagged disease), Shea's Partial <i>R</i>² indicates strength of IVs <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio.1001456-Shea1" target="_blank">[75]</a>. The institutions variable is not included as an IV for income and therefore 4b and 3b are identical. Bold indicates significance at the 10% level. <i>n</i> = 139.</p>IV<p>Variable is instrumented.</p>J<p>Based on Hansen's J statistic.</p>a<p>Units×10⁻².</p>*<p>p≤0.10.</p>**<p>p≤0.05.</p>***<p>p≤0.01.</p

Multiple infections cause the appearance and expansion of the basin of attraction of poverty traps.

<p>For graphs (a) and (b) (dashed blue line) and (solid dark green line). Graph (b) is a magnified version of the initial portion of graph (a), while graph (c) is a magnified version of the initial portion of graph (b) showing a stable positive poverty trap. The filled circles denote stable equilibria while the open circle denotes an unstable equilibrium. Graph (e) is a magnified version of the initial portion of graph (d), while graph (f) is a magnified version of the initial portion of graph (e). Each curve in graphs (d–f) represents the structure of capital accumulation for different numbers of pathogens.</p